Enabling frictionless shopping of products from bulk packaging

ABSTRACT

A method for identifying products removed from bulk packaging includes receiving one or more images acquired by a camera arranged to capture interactions between a shopper and one or more bulk packages each configured to contain a plurality of products, analyzing the one or more images to identify the shopper and a particular bulk package among the one or more bulk packages with which the identified shopper interacted, receiving an output from at least one sensor configured to monitor changes associated with the particular bulk package, analyzing the output to determine a quantity of products removed from the particular bulk package by the identified shopper, and updating a virtual shopping cart associated with the identified shopper to include the determined quantity of products and an indication of a product type associated with the particular bulk package.

CROSS REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalApplication No. 63/091,009, filed on Oct. 13, 2020, and U.S. ProvisionalApplication No. 63/113,490, filed on Nov. 13, 2020. The foregoingapplications are incorporated herein by reference in their entirety.

BACKGROUND I. Technical Field

The present disclosure relates generally to systems, methods, anddevices for identifying products in retail stores, and more specificallyto systems, methods, and devices for capturing, collecting, andautomatically analyzing images of products displayed in retail storesfor purposes of providing one or more functions associated with theidentified products.

II. Background Information

Shopping in stores is a prevalent part of modern daily life. Storeowners (also known as “retailers”) stock a wide variety of products onstore shelves and add associated labels and promotions to the storeshelves. Typically, retailers have a set of processes and instructionsfor organizing products on the store shelves. The source of some ofthese instructions may include contractual obligations and otherpreferences related to the retailer methodology for placement ofproducts on the store shelves. Nowadays, many retailers and supplierssend people to stores to personally monitor compliance with the desiredproduct placement. Such a monitoring technique, however, may beinefficient and may result in nonuniform compliance among retailersrelative to various product-related guidelines. This technique may alsoresult in significant gaps in compliance, as it does not allow forcontinuous monitoring of dynamically changing product displays. Toincrease productivity, among other potential benefits, there is atechnological need to provide a dynamic solution that will automaticallymonitor retail spaces. Such a solution, for example and among otherfeatures, may automatically determine whether a disparity exists betweena desired product placement and an actual product placement.

The disclosed devices and methods are directed to providing new ways formonitoring retail establishments using image processing and supportingsensors.

SUMMARY

Embodiments consistent with the present disclosure provide systems,methods, and devices for capturing, collecting, and analyzing images ofproducts displayed in retail stores. For example, consistent with thedisclosed embodiments, an example system may receive an image depictinga store shelf having products displayed thereon, identify the productson the store shelf, and trigger an alert when disparity exists betweenthe desired product placement and the actual product placement.

In an embodiment, a non-transitory computer-readable medium may includeinstructions that when executed by a processor may cause the processorto perform a method for determining whether shoppers are eligible forfrictionless checkout. The method may comprise obtaining image datacaptured using a plurality of image sensors positioned in a retailstore; analyzing the image data to identify at least one shopper at oneor more locations of the retail store; detecting, based on the analysisof the image data, at least one product interaction event associatedwith an action of the at least one shopper at the one or more locationsof the retail store; based on the detected at least one productinteraction event, determining whether the at least one shopper iseligible for frictionless checkout; and in response to a determinationthat the at least one shopper is ineligible for frictionless checkout,causing delivery of an indicator that the at least one shopper isineligible for frictionless checkout.

In an embodiment, a method for determining whether shoppers are eligiblefor frictionless checkout may comprise obtaining image data capturedusing a plurality of image sensors positioned in a retail store;analyzing the image data to identify at least one shopper at one or morelocations of the retail store; detecting, based on the analysis of theimage data, at least one product interaction event associated with anaction of the at least one shopper at the one or more locations of theretail store; based on the detected at least one product interactionevent, determining whether the at least one shopper is eligible forfrictionless checkout; and in response to a determination that the atleast one shopper is ineligible for frictionless checkout, causingdelivery of an indicator that the at least one shopper is ineligible forfrictionless checkout.

In an embodiment, a system for determining whether shoppers are eligiblefor frictionless checkout may comprise at least one processor programmedto: obtain image data captured using a plurality of image sensorspositioned in a retail store; analyze the image data to identify atleast one shopper at one or more locations of the retail store; detect,based on the analysis of the image data, at least one productinteraction event associated with an action of the at least one shopperat the one or more locations of the retail store; based on the detectedat least one product interaction event, determine whether the at leastone shopper is eligible for frictionless checkout; and in response to adetermination that the at least one shopper is ineligible forfrictionless checkout, cause delivery of an indicator that the at leastone shopper is ineligible for frictionless checkout.

In an embodiment, a non-transitory computer-readable medium may includeinstructions that when executed by a processor cause the processor toperform a method for providing a visual indicator indicative of africtionless checkout status of at least a portion of a retail shelf.The method may include receiving an output from one or more retail storesensors; based on the output from the one or more retail store sensors,determining a frictionless checkout eligibility status associated withthe at least a portion of the retail shelf, wherein the frictionlesscheckout eligibility status is indicative of whether the at least aportion of the retail shelf includes one or more items eligible forfrictionless checkout; and causing a display of an automaticallygenerated visual indicator indicating the frictionless checkouteligibility status associated with the at least a portion of the retailshelf.

In an embodiment, a system may receive an output from one or more retailstore sensors. Based on the output from the one or more retail storesensors, the system may determine a frictionless checkout eligibilitystatus associated with the at least a portion of the retail shelf,wherein the frictionless checkout eligibility status is indicative ofwhether the at least a portion of the retail shelf includes one or moreitems eligible for frictionless checkout. Thereafter, the system maycause a display of an automatically generated visual indicatorindicating the frictionless checkout eligibility status associated withthe at least a portion of the retail shelf.

In an embodiment, a method may provide a visual indicator indicative ofa frictionless checkout status of at least a portion of a retail shelf.The method may include receiving an output from one or more retail storesensors; based on the output from the one or more retail store sensors,determining a frictionless checkout eligibility status associated withthe at least a portion of the retail shelf, wherein the frictionlesscheckout eligibility status is indicative of whether the at least aportion of the retail shelf includes one or more items eligible forfrictionless checkout; and causing a display of an automaticallygenerated visual indicator indicating the frictionless checkouteligibility status associated with the at least a portion of the retailshelf.

In an embodiment, a non-transitory computer-readable medium may includeinstructions that when executed by a processor cause the processor toperform a method for addressing a shopper's eligibility for frictionlesscheckout. The method may include identifying at least one shopper in aretail store designated as not eligible for frictionless checkout; inresponse to the identification of the at least one shopper designated asnot eligible for frictionless checkout, automatically identifying anineligibility condition associated with the at least one shopper'sdesignation as not eligible for frictionless checkout; determining oneor more actions for resolving the ineligibility condition; causingimplementation of the one or more actions for resolving theineligibility condition; receiving an indication of successfulcompletion of the one or more actions; and in response to receipt of theindication of successful completion of the one more actions, generatinga status indicator indicating that the at least one shopper is eligiblefor frictionless checkout and storing the generated status indicator ina memory.

In an embodiment, a system for addressing a shopper's eligibility forfrictionless checkout may include at least one processing unitconfigured to: identify at least one shopper in a retail storedesignated as not eligible for frictionless checkout; in response to theidentification of the at least one shopper designated as not eligiblefor frictionless checkout, automatically identify an ineligibilitycondition associated with the at least one shopper's designation as noteligible for frictionless checkout; determine one or more actions forresolving the ineligibility condition; cause implementation of the oneor more actions for resolving the ineligibility condition; receive anindication of successful completion of the one or more actions; and inresponse to receipt of the indication of successful completion of theone more actions, generate a status indicator indicating that the atleast one shopper is eligible for frictionless checkout and storing thegenerated status indicator in a memory.

In an embodiment, a non-transitory computer-readable medium may includeinstructions that when executed by a processor cause the processor toperform a method for addressing a shopper's eligibility for frictionlesscheckout. The method may include receiving output from at least onesensor positioned in a retail store; analyzing the first data to detectan ambiguous product interaction event involving a first shopper and asecond shopper; in response to detection of the ambiguous productinteraction event, designating both the first shopper and the secondshopper as ineligible for frictionless checkout; detecting an actiontaken by the first shopper, wherein the action enables resolution ofambiguity associated with the product interaction event; and in responseto detection of the action taken by the first shopper, designating thesecond shopper as eligible for frictionless checkout.

In an embodiment, a non-transitory computer-readable medium includesinstructions that when executed by a processor cause the processor toperform a method for updating virtual shopping carts of shoppers withpay-by-weight products. The method may comprise receiving one or moreimages captured by one or more image sensors, wherein the one or moreimages depict product interactions between a store associate and aplurality of shoppers, wherein each of the product interactions involvesat least one pay-by-weight product; analyzing the one or more images toidentify the product interactions and to associate the at least onepay-by-weight product involved with each product interaction with aparticular shopper among the plurality of shoppers; providing anotification to the store associate requesting supplemental informationto assist in the association of the at least one pay-by-weight productinvolved with a selected product interaction with the particular shopperamong the plurality of shoppers; receiving the requested supplementalinformation from the store associate; using the analysis of the one ormore images and the requested supplemental information to determine theassociation of the at least one pay-by-weight product involved with theselected product interaction with the particular shopper among theplurality of shoppers; and updating a virtual shopping cart of theparticular shopper among the plurality of shoppers with the at least onepay-by-weight product involved with the selected product interaction.

In an embodiment, a method for updating virtual shopping carts ofshoppers with pay-by-weight products may comprise receiving one or moreimages captured by one or more image sensors, wherein the one or moreimages depict product interactions between a store associate and aplurality of shoppers, wherein each of the product interactions involvesat least one pay-by-weight product; analyzing the one or more images toidentify the product interactions and to associate the at least onepay-by-weight product involved with each product interaction with aparticular shopper among the plurality of shoppers; providing anotification to the store associate requesting supplemental informationto assist in the association of the at least one pay-by-weight productinvolved with a selected product interaction with the particular shopperamong the plurality of shoppers; receiving the requested supplementalinformation from the store associate; using the analysis of the one ormore images and the requested supplemental information to determine theassociation of the at least one pay-by-weight product involved with theselected product interaction with the particular shopper among theplurality of shoppers; and updating a virtual shopping cart of theparticular shopper among the plurality of shoppers with the at least onepay-by-weight product involved with the selected product interaction.

In an embodiment, a system for updating virtual shopping carts ofshoppers with pay-by-weight products may comprise a memory storinginstructions; and at least one processor programmed to execute thestored instructions to: receive one or more images captured by one ormore image sensors, wherein the one or more images depict productinteractions between a store associate and a plurality of shoppers,wherein each of the product interactions involves at least onepay-by-weight product; analyze the one or more images to identify theproduct interactions and to associate the at least one pay-by-weightproduct involved with each product interaction with a particular shopperamong the plurality of shoppers; provide a notification to the storeassociate requesting supplemental information to assist in theassociation of the at least one pay-by-weight product involved with aselected product interaction with the particular shopper among theplurality of shoppers; receive the requested supplemental informationfrom the store associate; use the analysis of the one or more images andthe requested supplemental information to determine the association ofthe at least one pay-by-weight product involved with the selectedproduct interaction with the particular shopper among the plurality ofshoppers; and update a virtual shopping cart of the particular shopperamong the plurality of shoppers with the at least one pay-by-weightproduct involved with the selected product interaction.

In an embodiment, a non-transitory computer-readable medium may includeinstructions that, when executed by a processor, cause the processor toperform a method that includes receiving one or more images acquired bya camera arranged to capture interactions between a shopper and one ormore bulk packages each configured to contain a plurality of products,and analyzing the one or more images to identify the shopper and aparticular bulk package among the one or more bulk packages with whichthe identified shopper interacted. The method also includes receiving anoutput from at least one sensor configured to monitor changes associatedwith the particular bulk package, and analyzing the output to determinea quantity of products removed from the particular bulk package by theidentified shopper. The method further includes updating a virtualshopping cart associated with the identified shopper to include thedetermined quantity of products and an indication of a product typeassociated with the particular bulk package.

In an embodiment, a system for identifying products removed from bulkpackaging may include at least one processing unit configured to receiveone or more images acquired by a camera arranged to capture interactionsbetween a shopper and one or more bulk packages each configured tocontain a plurality of products; analyze the one or more images toidentify the shopper and a particular bulk package among the one or morebulk packages with which the identified shopper interacted; receive anoutput from at least one sensor configured to monitor changes associatedwith the particular bulk package; analyze the output to determine aquantity of products removed from the particular bulk package by theidentified shopper; and update a virtual shopping cart associated withthe identified shopper to include the determined quantity of productsand an indication of a product type associated with the particular bulkpackage.

In an embodiment, a non-transitory computer-readable medium includinginstructions that when executed by a processor cause the processor toperform a method that includes receiving an output from one or morespatial sensors arranged to capture interactions between a shopper andone or more bulk packages each configured to contain a plurality ofproducts, and analyzing the output from the one or more sensors toidentify the shopper and a particular bulk package among the one or morebulk packages with which the identified shopper interacted. The methodalso includes receiving an output from at least one additional sensorconfigured to monitor changes associated with the particular bulkpackage, and analyzing the output from the at least one additionalsensor to determine a quantity of products removed from the particularbulk package by the identified shopper. The method further includesupdating a virtual shopping cart associated with the identified shopperto include the determined quantity of products and an indication of aproduct type associated with the particular bulk package.

In an embodiment, a method for identifying products removed from bulkpackaging may include receiving an output from one or more spatialsensors arranged to capture interactions between a shopper and one ormore bulk packages each configured to contain a plurality of products;analyzing the output from the one or more sensors to identify theshopper and a particular bulk package among the one or more bulkpackages with which the identified shopper interacted; receiving anoutput from at least one additional sensor configured to monitor changesassociated with the particular bulk package; analyzing the output fromthe at least one additional sensor to determine a quantity of productsremoved from the particular bulk package by the identified shopper; andupdating a virtual shopping cart associated with the identified shopperto include the determined quantity of products and an indication of aproduct type associated with the particular bulk package.

In an embodiment, a non-transitory computer-readable medium may includeinstructions that when executed by at least one processor cause the atleast one processor to perform a method for controlling a detail levelof shopping data provided to frictionless shoppers. The method mayinclude receiving image data captured using one or more image sensors ina retail store; analyzing the image data to detect a shopper in theretail store; determining a likelihood that the shopper will be involvedin shoplifting; and controlling a detail level associated withfrictionless shopping data provided to the shopper based on thedetermined likelihood that the shopper will be involved in shoplifting.

In an embodiment, a system may control a detail level of shopping dataprovided to frictionless shoppers. The system may include at least oneprocessor configured to receive image data captured using one or moreimage sensors in a retail store and analyze the image data to detect ashopper in the retail store. The at least one processor may furtherdetermine a likelihood that the shopper will be involved in shoplifting.Thereafter, the at least one processor may control a detail levelassociated with frictionless shopping data provided to the shopper basedon the determined likelihood that the shopper will be involved inshoplifting.

In an embodiment, a method may control a detail level of shopping dataprovided to frictionless shoppers. The method may include receivingimage data captured using one or more image sensors in a retail store;analyzing the image data to detect a shopper in the retail store;determining a likelihood that the shopper will be involved inshoplifting; and controlling a detail level associated with frictionlessshopping data provided to the shopper based on the determined likelihoodthat the shopper will be involved in shoplifting.

In an embodiment, a system may deliver shopping data for frictionlessshoppers. The system may include at least one processor configured toreceive image data captured using one or more image sensors in a retailstore and analyze the image data to identify a plurality of productinteraction events for at least one shopper in the retail store. The atleast one processor is further configured to determine shopping dataassociated with the plurality of product interaction events, anddetermine a likelihood that the at least one shopper will be involved inshoplifting. Based on the determined likelihood, the at least oneprocessor may determine an update rate for updating the at least oneshopper with the shopping data; and deliver the shopping data to the atleast one shopper at the determined update rate.

In an embodiment, a method may deliver shopping data for frictionlessshoppers. The method may include receiving image data captured using oneor more image sensors in a retail store; analyzing the image data toidentify a plurality of product interaction events for at least oneshopper in the retail store; determining shopping data associated withthe plurality of product interaction events; determining a likelihoodthat the at least one shopper will be involved in shoplifting; based onthe determined likelihood, determining an update rate for updating theat least one shopper with the shopping data; and delivering the shoppingdata to the at least one shopper at the determined update rate.

In an embodiment, a non-transitory computer-readable medium may includeinstructions that when executed by at least one processor cause the atleast one processor to perform a method for tracking frictionlessshopping eligibility relative to individual shopping receptacles. Themethod may include obtaining image data captured using a plurality ofimage sensors positioned in a retail store; analyzing the image data toidentify a shopper at one or more locations of the retail store;detecting, based on the analysis of the image data, a first productinteraction event involving a first shopping receptacle associated withthe shopper and a second product interaction event involving a secondshopping receptacle associated with the shopper; based on the detectedfirst product interaction event, determining whether the first shoppingreceptacle is eligible for frictionless checkout; based on the detectedsecond product interaction event, determining whether the secondshopping receptacle is eligible for frictionless checkout; and inresponse to a determination that the first shopping receptacle or thesecond shopping receptacle is ineligible for frictionless checkout,causing delivery of an indicator identifying which of the first shoppingreceptacle or the second shopping receptacle is ineligible forfrictionless checkout.

In an embodiment, a system may track frictionless shopping eligibilityrelative to individual shopping receptacles. The system may include atleast one processor programmed to obtain image data captured using aplurality of image sensors positioned in a retail store. Thereafter, theat least one processor may analyze the image data to identify a shopperat one or more locations of the retail store, and detect, based on theanalysis of the image data, a first product interaction event involvinga first shopping receptacle associated with the shopper and a secondproduct interaction event involving a second shopping receptacleassociated with the shopper. Based on the detected first productinteraction event, the at least one processor may determine whether thefirst shopping receptacle is eligible for frictionless checkout. Basedon the detected second product interaction event, the at least oneprocessor may determine whether the second shopping receptacle iseligible for frictionless checkout. Thereafter, in response to adetermination that the first shopping receptacle or the second shoppingreceptacle is ineligible for frictionless checkout, the at least oneprocessor may cause delivery of an indicator identifying which of thefirst shopping receptacle or the second shopping receptacle isineligible for frictionless checkout.

In an embodiment, a method may track frictionless shopping eligibilityrelative to individual shopping receptacles. The method may includeobtaining image data captured using a plurality of image sensorspositioned in a retail store; analyzing the image data to identify ashopper at one or more locations of the retail store; detecting, basedon the analysis of the image data, a first product interaction eventinvolving a first shopping receptacle associated with the shopper and asecond product interaction event involving a second shopping receptacleassociated with the shopper; based on the detected first productinteraction event, determining whether the first shopping receptacle iseligible for frictionless checkout; based on the detected second productinteraction event, determining whether the second shopping receptacle iseligible for frictionless checkout; and in response to a determinationthat the first shopping receptacle or the second shopping receptacle isineligible for frictionless checkout, causing delivery of an indicatoridentifying which of the first shopping receptacle or the secondshopping receptacle is ineligible for frictionless checkout.

In an embodiment, a non-transitory computer-readable medium may includeinstructions that, when executed by at least one processor, cause the atleast one processor to perform a method for automatically updating aplurality of virtual shopping carts. The method may include receivingimage data captured in a retail store. A first shopping receptacle and asecond shopping receptacle may be represented in the received imagedata. The method may also include determining that the first shoppingreceptacle is associated with a first virtual shopping cart and that thesecond shopping receptacle is associated with a second virtual shoppingcart different from the first virtual shopping cart, and analyzing thereceived image data to detect a shopper placing a first product in thefirst shopping receptacle and to detect the shopper placing a secondproduct in the second shopping receptacle. The method may furtherinclude, in response to detecting that the shopper placed the firstproduct in the first shopping receptacle, automatically updating thefirst virtual shopping cart to include information associated with thefirst product, and in response to detecting that the shopper placed thesecond product in the second shopping receptacle, automatically updatingthe second virtual shopping cart to include information associated withthe second product.

In an embodiment, a method for automatically updating a plurality ofvirtual shopping carts is provided. The method may include receivingimage data captured in a retail store. A first shopping receptacle and asecond shopping receptacle may be represented in the received imagedata. The method may also include determining that the first shoppingreceptacle is associated with a first virtual shopping cart and that thesecond shopping receptacle is associated with a second virtual shoppingcart different from the first virtual shopping cart, and analyzing thereceived image data to detect a shopper placing a first product in thefirst shopping receptacle and to detect the shopper placing a secondproduct in the second shopping receptacle. The method may furtherinclude, in response to detecting that the shopper placed the firstproduct in the first shopping receptacle, automatically updating thefirst virtual shopping cart to include information associated with thefirst product, and in response to detecting that the shopper placed thesecond product in the second shopping receptacle, automatically updatingthe second virtual shopping cart to include information associated withthe second product.

In an embodiment, a system for automatically updating a plurality ofvirtual shopping carts may comprise at least one processor. The at leastone processor may be configured to receive image data captured in aretail store. A first shopping receptacle and a second shoppingreceptacle may be represented in the received image data. The at leastone processor may also be configured to determine that the firstshopping receptacle is associated with a first virtual shopping cart andthat the second shopping receptacle is associated with a second virtualshopping cart different from the first virtual shopping cart, and toanalyze the received image data to detect a shopper placing a firstproduct in the first shopping receptacle and to detect the shopperplacing a second product in the second shopping receptacle. The at leastone processor may be further configured to, in response to detectingthat the shopper placed the first product in the first shoppingreceptacle, automatically updating the first virtual shopping cart toinclude information associated with the first product, and in responseto detecting that the shopper placed the second product in the secondshopping receptacle, automatically updating the second virtual shoppingcart to include information associated with the second product.

In an embodiment, a non-transitory computer-readable medium may includeinstructions that when executed by at least one processor cause the atleast one processor to perform a method for using an electronic shoppinglist to resolve ambiguity associated with a selected product. The methodmay include accessing an electronic shopping list associated with acustomer of a retail store; receiving image data captured using one ormore image sensors in the retail store; analyzing the image data todetect a product selection event involving a shopper; identifying aproduct associated with the detected product selection event based onanalysis of the image data and further based on the electronic shoppinglist; and in response to identification of the product, updating avirtual shopping cart associated with the shopper.

In an embodiment, a method for using an electronic shopping list toresolve ambiguity associated with a selected product is disclosed. Themethod may comprise accessing an electronic shopping list associatedwith a customer of a retail store; receiving image data captured usingone or more image sensors in the retail store; analyzing the image datato detect a product selection event involving a shopper; identifying aproduct associated with the detected product selection event based onanalysis of the image data and further based on the electronic shoppinglist; and in response to identification of the product, updating avirtual shopping cart associated with the shopper.

In an embodiment, a system for using an electronic shopping list toresolve ambiguity associated with a selected product may comprise atleast one processor. The at least one processor may be programmed toaccess an electronic shopping list associated with a customer of aretail store; receive image data captured using one or more imagesensors in the retail store; analyze the image data to detect a productselection event involving a shopper; identify a product associated withthe detected product selection event based on analysis of the image dataand further based on the electronic shopping list; and in response toidentification of the product, update a virtual shopping cart associatedwith the shopper.

In an embodiment, a non-transitory computer-readable medium includesinstructions that when executed by at least processor cause the at leastprocessor to perform a method for automatically updating electronicshopping lists of customers of retail stores. The method may includeaccessing an electronic shopping list of a customer of a retail store,the electronic shopping list including at least one product associatedwith a shopping order; receiving image data from a plurality of imagesensors mounted in the retail store; analyzing the image data to predictan inventory shortage of the at least one product included on theelectronic shopping list, wherein the predicted inventory shortage isexpected to occur prior to fulfillment of the shopping order; andautomatically updating the electronic shopping list based on thepredicted inventory shortage of the at least one product.

In an embodiment, a method for automatically updating electronicshopping lists of customers of retail stores is disclosed. The methodmay comprise accessing an electronic shopping list of a customer of aretail store, the electronic shopping list including at least oneproduct associated with a shopping order; receiving image data from aplurality of image sensors mounted in the retail store; analyzing theimage data to predict an inventory shortage of the at least one productincluded on the electronic shopping list, wherein the predictedinventory shortage is expected to occur prior to fulfillment of theshopping order; and automatically updating the electronic shopping listbased on the predicted inventory shortage of the at least one product.

In an embodiment, a system for automatically updating electronicshopping lists of customers of retail stores may comprise at least oneprocessor. The at least one processor may be programmed to access anelectronic shopping list of a customer of a retail store, the electronicshopping list including at least one product associated with a shoppingorder; receive image data from a plurality of image sensors mounted inthe retail store; analyze the image data to predict an inventoryshortage of the at least one product included on the electronic shoppinglist, wherein the predicted inventory shortage is expected to occurprior to fulfillment of the shopping order; and automatically update theelectronic shopping list based on the predicted inventory shortage ofthe at least one product.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1 is an illustration of an exemplary system for analyzinginformation collected from a retail store;

FIG. 2 is a block diagram that illustrates some of the components of animage processing system, consistent with the present disclosure;

FIG. 3 is a block diagram that illustrates an exemplary embodiment of acapturing device, consistent with the present disclosure;

FIG. 4A is a schematic illustration of an example configuration forcapturing image data in a retail store, consistent with the presentdisclosure;

FIG. 4B is a schematic illustration of another example configuration forcapturing image data in a retail store, consistent with the presentdisclosure;

FIG. 4C is a schematic illustration of another example configuration forcapturing image data in a retail store, consistent with the presentdisclosure;

FIG. 5A is an illustration of an example system for acquiring images ofproducts in a retail store, consistent with the present disclosure.

FIG. 5B is an illustration of a shelf-mounted camera unit included in afirst housing of the example system of FIG. 5A, consistent with thepresent disclosure.

FIG. 5C is an exploded view illustration of a processing unit includedin a second housing of the example system of FIG. 5A, consistent withthe present disclosure.

FIG. 6A is a top view representation of an aisle in a retail store withmultiple image acquisition systems deployed thereon for acquiring imagesof products, consistent with the present disclosure.

FIG. 6B is a perspective view representation of part of a retailshelving unit with multiple image acquisition systems deployed thereonfor acquiring images of products, consistent with the presentdisclosure.

FIG. 6C provides a diagrammatic representation of how the exemplarydisclosed image acquisition systems may be positioned relative to retailshelving to acquire product images, consistent with the presentdisclosure.

FIG. 7A provides a flowchart of an exemplary method for acquiring imagesof products in retail store, consistent with the present disclosure.

FIG. 7B provides a flowchart of a method for acquiring images ofproducts in retail store, consistent with the present disclosure.

FIG. 8A is a schematic illustration of an example configuration fordetecting products and empty spaces on a store shelf, consistent withthe present disclosure;

FIG. 8B is a schematic illustration of another example configuration fordetecting products and empty spaces on a store shelf, consistent withthe present disclosure;

FIG. 9 is a schematic illustration of example configurations fordetection elements on store shelves, consistent with the presentdisclosure;

FIG. 10A illustrates an exemplary method for monitoring planogramcompliance on a store shelf, consistent with the present disclosure;

FIG. 10B is illustrates an exemplary method for triggering imageacquisition based on product events on a store shelf, consistent withthe present disclosure;

FIG. 11A is a schematic illustration of an example output for a marketresearch entity associated with the retail store, consistent with thepresent disclosure;

FIG. 11B is a schematic illustration of an example output for a supplierof the retail store, consistent with the present disclosure;

FIG. 11C is a schematic illustration of an example output for a managerof the retail store, consistent with the present disclosure;

FIG. 11D is a schematic illustration of two examples outputs for a storeassociate of the retail store, consistent with the present disclosure;and

FIG. 11E is a schematic illustration of an example output for an onlinecustomer of the retail store, consistent with the present disclosure.

FIG. 12A illustrates an example of a shopper interacting with a productin a retail store, consistent with the present disclosure;

FIG. 12B illustrates an example of a plurality of shoppers interactingwith products in a retail store, consistent with the present disclosure;

FIG. 12C illustrates a top view of an exemplary retail store showing apath followed by a shopper, consistent with the present disclosure;

FIG. 13A illustrates an example of a device displaying an indicator,consistent with the present disclosure;

FIG. 13B illustrates additional examples of devices capable ofdisplaying an indicator, consistent with the present disclosure; and

FIG. 14 illustrates an exemplary method for determining whether shoppersare eligible for frictionless checkout, consistent with the presentdisclosure.

FIG. 15A is a schematic illustration of an example configuration forproviding visual indicators indicating the frictionless checkouteligibility statuses of different portions of retail shelves, consistentwith the present disclosure.

FIG. 15B is a schematic illustration of another example configurationfor providing visual indicators indicating the frictionless checkouteligibility statuses of different portions of retail shelves, consistentwith the present disclosure.

FIG. 15C is a schematic illustration of another example configurationfor providing visual indicators indicating the frictionless checkouteligibility statuses of different portions of retail shelves, consistentwith the present disclosure.

FIG. 15D is a schematic illustration of another example configurationfor providing visual indicators indicating the frictionless checkouteligibility statuses of different portions of retail shelves, consistentwith the present disclosure.

FIG. 16 is a block diagram illustrating an exemplary embodiment of amemory device containing software modules for executing methodsconsistent with the present disclosure.

FIG. 17A is a flowchart of an exemplary process for updating a visualindicator indicating the frictionless checkout eligibility status of aretail shelf, consistent with the present disclosure.

FIG. 17B is a flowchart of an exemplary method for providing a visualindicator indicative of a frictionless checkout status of at least aportion of a retail shelf consistent with the present disclosure.

FIG. 18 illustrates an example ambiguous product interaction event thatmay be detected, consistent with the disclosed embodiments.

FIG. 19A illustrates an example shopper profile that may be associatedwith a shopper, consistent with the disclosed embodiments.

FIG. 19B is a diagrammatic illustration of various actions that mayresult in frictionless checkout status being granted or restored,consistent with the disclosed embodiments.

FIG. 20A is a flowchart showing an exemplary method for addressing ashopper's eligibility for frictionless checkout, consistent with thepresent disclosure.

FIG. 20B is a flowchart showing another exemplary method for addressinga shopper's eligibility for frictionless checkout, consistent with thepresent disclosure.

FIG. 21 illustrates an example of one or more shoppers interacting witha store associate to purchase a pay-by-weight product in a retail store,consistent with the present disclosure.

FIG. 22 illustrates an example of a device displaying a notificationsent to the store associate, consistent with the present disclosure.

FIG. 23 illustrates an exemplary method for updating virtual shoppingcarts of shoppers with pay-by-weight products, consistent with thepresent disclosure.

FIG. 24 is an illustration of an exemplary system for identifyingproducts removed from bulk packaging, consistent with embodiments of thepresent disclosure.

FIG. 25A is a schematic illustration of an example configuration of aretail store, consistent with embodiments of the present disclosure.

FIG. 25B is a schematic illustration of a front view of a shelving unitin a retail store, consistent with embodiments of the presentdisclosure.

FIG. 26A includes a flowchart representing an exemplary method foridentifying products removed from bulk packaging, consistent with anembodiment of the present disclosure.

FIG. 26B includes a flowchart representing an exemplary method foridentifying products removed from bulk packaging, consistent withanother embodiment of the present disclosure.

FIG. 27 is a top view representation of an aisle in a retail store withmultiple image sensors deployed thereon for identifying a plurality ofproduct interaction events of a shopper, consistent with the presentdisclosure.

FIG. 28 is a block diagram illustrating an exemplary embodiment of amemory device containing software modules for executing methodsconsistent with the present disclosure.

FIG. 29 is a table describing different detail levels of shopping datadelivered to shoppers in corresponding use cases, consistent with thepresent disclosure.

FIG. 30 is a diagram showing example timelines illustrating twodifferent update rates for providing shopping data, consistent with thepresent disclosure.

FIG. 31 is a flowchart of an exemplary method for controlling a detaillevel of shopping data provided to frictionless shoppers, consistentwith the present disclosure.

FIG. 32 is a flowchart of an exemplary method for delivering shoppingdata to frictionless shoppers at a determined update rate, consistentwith the present disclosure.

FIG. 33A is a schematic illustration of a semi frictionless checkoutprocess, consistent with the present disclosure.

FIG. 33B is a schematic illustration of an example visual indicatorshowing the frictionless checkout eligibility status of a shoppingreceptacle, consistent with the present disclosure.

FIG. 34 is a block flow diagram illustrating an example process fordetermining the frictionless checkout eligibility statuses of twoshopping receptacles, consistent with the present disclosure.

FIG. 35 is a flowchart of an exemplary process for tracking frictionlessshopping eligibility relative to individual shopping receptacles,consistent with the present disclosure.

FIG. 36 is an illustration of an exemplary system for frictionlessshopping for multiple shopping accounts, consistent with someembodiments of the present disclosure.

FIG. 37A is a schematic illustration of an example configuration of aretail store, consistent with an embodiment of the present disclosure.

FIG. 37B is a schematic illustration of an example configuration of aretail store, consistent with another embodiment of the presentdisclosure.

FIGS. 38A, 38B, and 38C include flowcharts representing an exemplarymethod for automatically updating a plurality of virtual shopping carts,consistent with an embodiment of the present disclosure.

FIG. 39 illustrates an example electronic shopping list associated witha customer, consistent with the disclosed embodiments.

FIG. 40A illustrates an example product interaction event that may bedetected, consistent with the disclosed embodiments.

FIG. 40B is a diagrammatic illustration of an example process forresolving an ambiguity based on a shopping list, consistent with thedisclosed embodiments.

FIG. 41 illustrates example information that may be used to identify aproduct or to confirm a product identification, consistent with thedisclosed embodiments.

FIG. 42 is a flowchart of an exemplary method for using an electronicshopping list to resolve ambiguity associated with a selected product,consistent with the present disclosure.

FIG. 43 illustrates an example image that may be analyzed to predict aninventory shortage, consistent with the present disclosure.

FIG. 44 is a diagrammatic illustration of various updates to anelectronic shopping list that may be performed, consistent with thepresent disclosure.

FIG. 45 illustrates an example shopping path that may be generated basedon an updated electronic shopping list, consistent with the presentdisclosure.

FIG. 46 is a flowchart of an exemplary method for automatically updatingelectronic shopping lists of customers of retail stores, consistent withthe present disclosure.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions, or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

The present disclosure is directed to systems and methods for processingimages captured in a retail store. As used herein, the term “retailstore” or simply “store” refers to an establishment offering productsfor sale by direct selection by customers physically or virtuallyshopping within the establishment. The retail store may be anestablishment operated by a single retailer (e.g., supermarket) or anestablishment that includes stores operated by multiple retailers (e.g.,a shopping mall). Embodiments of the present disclosure includereceiving an image depicting a store shelf having at least one productdisplayed thereon. As used herein, the term “store shelf” or simply“shelf” refers to any suitable physical structure which may be used fordisplaying products in a retail environment. In one embodiment the storeshelf may be part of a shelving unit including a number of individualstore shelves. In another embodiment, the store shelf may include adisplay unit having a single-level or multi-level surfaces.

Consistent with the present disclosure, the system may process imagesand image data acquired by a capturing device to determine informationassociated with products displayed in the retail store. The term“capturing device” refers to any device configured to acquire image datarepresentative of products displayed in the retail store. Examples ofcapturing devices may include a digital camera, a time-of-flight camera,a stereo camera, an active stereo camera, a depth camera, a Lidarsystem, a laser scanner, CCD based devices, or any other sensor basedsystem capable of converting received light into electric signals. Theterm “image data” refers to any form of data generated based on opticalsignals in the near-infrared, infrared, visible, and ultravioletspectrums (or any other suitable radiation frequency range). Consistentwith the present disclosure, the image data may include pixel datastreams, digital images, digital video streams, data derived fromcaptured images, and data that may be used to construct a 3D image. Theimage data acquired by a capturing device may be transmitted by wired orwireless transmission to a remote server. In one embodiment, thecapturing device may include a stationary camera with communicationlayers (e.g., a dedicated camera fixed to a store shelf, a securitycamera, etc.). Such an embodiment is described in greater detail belowwith reference to FIG. 4A. In another embodiment, the capturing devicemay include a handheld device (e.g., a smartphone, a tablet, a mobilestation, a personal digital assistant, a laptop, and more) or a wearabledevice (e.g., smart glasses, a smartwatch, a clip-on camera). Such anembodiment is described in greater detail below with reference to FIG.4B. In another embodiment, the capturing device may include a roboticdevice with one or more cameras operated remotely or autonomously (e.g.,an autonomous robotic device, a drone, a robot on a track, and more).Such an embodiment is described in greater detail below with referenceto FIG. 4C.

In some embodiments, the capturing device may include one or more imagesensors. The term “image sensor” refers to a device capable of detectingand converting optical signals in the near-infrared, infrared, visible,and ultraviolet spectrums into electrical signals. The electricalsignals may be used to form image data (e.g., an image or a videostream) based on the detected signal. Examples of image sensors mayinclude semiconductor charge-coupled devices (CCD), active pixel sensorsin complementary metal-oxide-semiconductor (CMOS), or N-typemetal-oxide-semiconductors (NMOS, Live MOS). In some cases, the imagesensor may be part of a camera included in the capturing device.

Embodiments of the present disclosure further include analyzing imagesto detect and identify different products. As used herein, the term“detecting a product” may broadly refer to determining an existence ofthe product. For example, the system may determine the existence of aplurality of distinct products displayed on a store shelf. By detectingthe plurality of products, the system may acquire different detailsrelative to the plurality of products (e.g., how many products on astore shelf are associated with a same product type), but it does notnecessarily gain knowledge of the type of product. In contrast, the term“identifying a product” may refer to determining a unique identifierassociated with a specific type of product that allows inventorymanagers to uniquely refer to each product type in a product catalogue.Additionally or alternatively, the term “identifying a product” mayrefer to determining a unique identifier associated with a specificbrand of products that allows inventory managers to uniquely refer toproducts, e.g., based on a specific brand in a product catalogue.Additionally or alternatively, the term “identifying a product” mayrefer to determining a unique identifier associated with a specificcategory of products that allows inventory managers to uniquely refer toproducts, e.g., based on a specific category in a product catalogue. Insome embodiments, the identification may be made based at least in parton visual characteristics of the product (e.g., size, shape, logo, text,color, etc.). The unique identifier may include any codes that may beused to search a catalog, such as a series of digits, letters, symbols,or any combinations of digits, letters, and symbols. Consistent with thepresent disclosure, the terms “determining a type of a product” and“determining a product type” may also be used interchangeably in thisdisclosure with reference to the term “identifying a product.”

Embodiments of the present disclosure further include determining atleast one characteristic of the product for determining the type of theproduct. As used herein, the term “characteristic of the product” refersto one or more visually discernable features attributed to the product.Consistent with the present disclosure, the characteristic of theproduct may assist in classifying and identifying the product. Forexample, the characteristic of the product may be associated with theornamental design of the product, the size of the product, the shape ofthe product, the colors of the product, the brand of the product, a logoor text associated with the product (e.g., on a product label), andmore. In addition, embodiments of the present disclosure further includedetermining a confidence level associated with the determined type ofthe product. The term “confidence level” refers to any indication,numeric or otherwise, of a level (e.g., within a predetermined range)indicative of an amount of confidence the system has that the determinedtype of the product is the actual type of the product. For example, theconfidence level may have a value between 1 and 10, alternatively, theconfidence level may be expressed as a percentage.

In some cases, the system may compare the confidence level to athreshold. The term “threshold” as used herein denotes a referencevalue, a level, a point, or a range of values, for which, when theconfidence level is above it (or below it depending on a particular usecase), the system may follow a first course of action and, when theconfidence level is below it (or above it depending on a particular usecase), the system may follow a second course of action. The value of thethreshold may be predetermined for each type of product or may bedynamically selected based on different considerations. In oneembodiment, when the confidence level associated with a certain productis below a threshold, the system may obtain contextual information toincrease the confidence level. As used herein, the term “contextualinformation” (or “context”) refers to any information having a direct orindirect relationship with a product displayed on a store shelf. In someembodiments, the system may retrieve different types of contextualinformation from captured image data and/or from other data sources. Insome cases, contextual information may include recognized types ofproducts adjacent to the product under examination. In other cases,contextual information may include text appearing on the product,especially where that text may be recognized (e.g., via OCR) andassociated with a particular meaning. Other examples of types ofcontextual information may include logos appearing on the product, alocation of the product in the retail store, a brand name of theproduct, a price of the product, product information collected frommultiple retail stores, product information retrieved from a catalogassociated with a retail store, etc.

Reference is now made to FIG. 1 , which shows an example of a system 100for analyzing information collected from retail stores 105 (for example,retail store 105A, retail store 105B, and retail store 105C). In oneembodiment, system 100 may represent a computer-based system that mayinclude computer system components, desktop computers, workstations,tablets, handheld computing devices, memory devices, and/or internalnetwork(s) connecting the components. System 100 may include or beconnected to various network computing resources (e.g., servers,routers, switches, network connections, storage devices, etc.) necessaryto support the services provided by system 100. In one embodiment,system 100 may enable identification of products in retail stores 105based on analysis of captured images. In another embodiment, system 100may enable a supply of information based on analysis of captured imagesto a market research entity 110 and to different suppliers 115 of theidentified products in retail stores 105 (for example, supplier 115A,supplier 115B, and supplier 115C). In another embodiment, system 100 maycommunicate with a user 120 (sometimes referred to herein as a customer,but which may include individuals associated with a retail environmentother than customers, such as store associate, data collection agent,etc.) about different products in retail stores 105. In one example,system 100 may receive images of products captured by user 120. Inanother example, system 100 may provide to user 120 informationdetermined based on automatic machine analysis of images captured by oneor more capturing devices 125 associated with retail stores 105.

System 100 may also include an image processing unit 130 to execute theanalysis of images captured by the one or more capturing devices 125.Image processing unit 130 may include a server 135 operatively connectedto a database 140. Image processing unit 130 may include one or moreservers connected by a communication network, a cloud platform, and soforth. Consistent with the present disclosure, image processing unit 130may receive raw or processed data from capturing device 125 viarespective communication links, and provide information to differentsystem components using a network 150. Specifically, image processingunit 130 may use any suitable image analysis technique including, forexample, object recognition, object detection, image segmentation,feature extraction, optical character recognition (OCR), object-basedimage analysis, shape region techniques, edge detection techniques,pixel-based detection, artificial neural networks, convolutional neuralnetworks, etc. In addition, image processing unit 130 may useclassification algorithms to distinguish between the different productsin the retail store. In some embodiments, image processing unit 130 mayutilize suitably trained machine learning algorithms and models toperform the product identification. Network 150 may facilitatecommunications and data exchange between different system componentswhen these components are coupled to network 150 to enable output ofdata derived from the images captured by the one or more capturingdevices 125. In some examples, the types of outputs that imageprocessing unit 130 may generate may include identification of products,indicators of product quantity, indicators of planogram compliance,indicators of service-improvement events (e.g., a cleaning event, arestocking event, a rearrangement event, etc.), and various reportsindicative of the performances of retail stores 105. Additional examplesof the different outputs enabled by image processing unit 130 aredescribed below with reference to FIGS. 11A-11E and throughout thedisclosure.

Consistent with the present disclosure, network 150 may be any type ofnetwork (including infrastructure) that provides communications,exchanges information, and/or facilitates the exchange of informationbetween the components of system 100. For example, network 150 mayinclude or be part of the Internet, a Local Area Network, wirelessnetwork (e.g., a Wi-Fi/302.11 network), or other suitable connections.In other embodiments, one or more components of system 100 maycommunicate directly through dedicated communication links, such as, forexample, a telephone network, an extranet, an intranet, the Internet,satellite communications, off-line communications, wirelesscommunications, transponder communications, a local area network (LAN),a wide area network (WAN), a virtual private network (VPN), and soforth.

In one example configuration, server 135 may be a cloud server thatprocesses images received directly (or indirectly) from one or morecapturing device 125 and processes the images to detect and/or identifyat least some of the plurality of products in the image based on visualcharacteristics of the plurality of products. The term “cloud server”refers to a computer platform that provides services via a network, suchas the Internet. In this example configuration, server 135 may usevirtual machines that may not correspond to individual hardware. Forexample, computational and/or storage capabilities may be implemented byallocating appropriate portions of desirable computation/storage powerfrom a scalable repository, such as a data center or a distributedcomputing environment. In one example, server 135 may implement themethods described herein using customized hard-wired logic, one or moreApplication Specific Integrated Circuits (ASICs) or Field ProgrammableGate Arrays (FPGAs), firmware, and/or program logic which, incombination with the computer system, cause server 135 to be aspecial-purpose machine.

In another example configuration, server 135 may be part of a systemassociated with a retail store that communicates with capturing device125 using a wireless local area network (WLAN) and may provide similarfunctionality as a cloud server. In this example configuration, server135 may communicate with an associated cloud server (not shown) andcloud database (not shown). The communications between the store serverand the cloud server may be used in a quality enforcement process, forupgrading the recognition engine and the software from time to time, forextracting information from the store level to other data users, and soforth. Consistent with another embodiment, the communications betweenthe store server and the cloud server may be discontinuous (purposely orunintentional) and the store server may be configured to operateindependently from the cloud server. For example, the store server maybe configured to generate a record indicative of changes in productplacement that occurred when there was a limited connection (or noconnection) between the store server and the cloud server, and toforward the record to the cloud server once connection is reestablished.

As depicted in FIG. 1 , server 135 may be coupled to one or morephysical or virtual storage devices such as database 140. Server 135 mayaccess database 140 to detect and/or identify products. The detectionmay occur through analysis of features in the image using an algorithmand stored data. The identification may occur through analysis ofproduct features in the image according to stored product models.Consistent with the present embodiment, the term “product model” refersto any type of algorithm or stored product data that a processor mayaccess or execute to enable the identification of a particular productassociated with the product model. For example, the product model mayinclude a description of visual and contextual properties of theparticular product (e.g., the shape, the size, the colors, the texture,the brand name, the price, the logo, text appearing on the particularproduct, the shelf associated with the particular product, adjacentproducts in a planogram, the location within the retail store, etc.). Insome embodiments, a single product model may be used by server 135 toidentify more than one type of products, such as, when two or moreproduct models are used in combination to enable identification of aproduct. For example, in some cases, a first product model may be usedby server 135 to identify a product category (such models may apply tomultiple product types, e.g., shampoo, soft drinks, etc.), and a secondproduct model may be used by server 135 to identify the product type,product identity, or other characteristics associated with a product. Insome cases, such product models may be applied together (e.g., inseries, in parallel, in a cascade fashion, in a decision tree fashion,etc.) to reach a product identification. In other embodiments, a singleproduct model may be used by server 135 to identify a particular producttype (e.g., 6-pack of 16 oz Coca-Cola Zero).

Database 140 may be included on a volatile or non-volatile, magnetic,semiconductor, tape, optical, removable, non-removable, or other type ofstorage device or tangible or non-transitory computer-readable medium.Database 140 may also be part of server 135 or separate from server 135.When database 140 is not part of server 135, server 135 may exchangedata with database 140 via a communication link. Database 140 mayinclude one or more memory devices that store data and instructions usedto perform one or more features of the disclosed embodiments. In oneembodiment, database 140 may include any suitable databases, rangingfrom small databases hosted on a work station to large databasesdistributed among data centers. Database 140 may also include anycombination of one or more databases controlled by memory controllerdevices (e.g., server(s), etc.) or software. For example, database 140may include document management systems, Microsoft SQL databases,SharePoint databases, Oracle™ databases, Sybase™ databases, otherrelational databases, or non-relational databases, such as mongo andothers.

Consistent with the present disclosure, image processing unit 130 maycommunicate with output devices 145 to present information derived basedon processing of image data acquired by capturing devices 125. The term“output device” is intended to include all possible types of devicescapable of outputting information from server 135 to users or othercomputer systems (e.g., a display screen, a speaker, a desktop computer,a laptop computer, mobile device, tablet, a PDA, etc.), such as 145A,145B, 145C and 145D. In one embodiment each of the different systemcomponents (i.e., retail stores 105, market research entity 110,suppliers 115, and users 120) may be associated with an output device145, and each system component may be configured to present differentinformation on the output device 145. In one example, server 135 mayanalyze acquired images including representations of shelf spaces. Basedon this analysis, server 135 may compare shelf spaces associated withdifferent products, and output device 145A may present market researchentity 110 with information about the shelf spaces associated withdifferent products. The shelf spaces may also be compared with salesdata, expired products data, and more. Consistent with the presentdisclosure, market research entity 110 may be a part of (or may workwith) supplier 115. In another example, server 135 may determine productcompliance to a predetermined planogram, and output device 145B maypresent to supplier 115 information about the level of productcompliance at one or more retail stores 105 (for example in a specificretail store 105, in a group of retail stores 105 associated withsupplier 115, in all retail stores 105, and so forth). The predeterminedplanogram may be associated with contractual obligations and/or otherpreferences related to the retailer methodology for placement ofproducts on the store shelves. In another example, server 135 maydetermine that a specific store shelf has a type of fault in the productplacement, and output device 145C may present to a manager of retailstore 105 a user-notification that may include information about acorrect display location of a misplaced product, information about astore shelf associated with the misplaced product, information about atype of the misplaced product, and/or a visual depiction of themisplaced product. In another example, server 135 may identify whichproducts are available on the shelf and output device 145D may presentto user 120 an updated list of products.

The components and arrangements shown in FIG. 1 are not intended tolimit the disclosed embodiments, as the system components used toimplement the disclosed processes and features may vary. In oneembodiment, system 100 may include multiple servers 135, and each server135 may host a certain type of service. For example, a first server mayprocess images received from capturing devices 125 to identify at leastsome of the plurality of products in the image, and a second server maydetermine from the identified products in retail stores 105 compliancewith contractual obligations between retail stores 105 and suppliers115. In another embodiment, system 100 may include multiple servers 135,a first type of servers 135 that may process information from specificcapturing devices 125 (e.g., handheld devices of data collection agents)or from specific retail stores 105 (e.g., a server dedicated to aspecific retail store 105 may be placed in or near the store). System100 may further include a second type of servers 135 that collect andprocess information from the first type of servers 135.

FIG. 2 is a block diagram representative of an example configuration ofserver 135. In one embodiment, server 135 may include a bus 200 (or anyother communication mechanism) that interconnects subsystems andcomponents for transferring information within server 135. For example,bus 200 may interconnect a processing device 202, a memory interface204, a network interface 206, and a peripherals interface 208 connectedto an I/O system 210.

Processing device 202, shown in FIG. 2 , may include at least oneprocessor configured to execute computer programs, applications,methods, processes, or other software to execute particular instructionsassociated with embodiments described in the present disclosure. Theterm “processing device” refers to any physical device having anelectric circuit that performs a logic operation. For example,processing device 202 may include one or more processors, integratedcircuits, microchips, microcontrollers, microprocessors, all or part ofa central processing unit (CPU), graphics processing unit (GPU), digitalsignal processor (DSP), field programmable gate array (FPGA), or othercircuits suitable for executing instructions or performing logicoperations. Processing device 202 may include at least one processorconfigured to perform functions of the disclosed methods such as amicroprocessor manufactured by Intel™, Nvidia™, manufactured by AMD™,and so forth. Processing device 202 may include a single core ormultiple core processors executing parallel processes simultaneously. Inone example, processing device 202 may be a single core processorconfigured with virtual processing technologies. Processing device 202may implement virtual machine technologies or other technologies toprovide the ability to execute, control, run, manipulate, store, etc.,multiple software processes, applications, programs, etc. In anotherexample, processing device 202 may include a multiple-core processorarrangement (e.g., dual, quad core, etc.) configured to provide parallelprocessing functionalities to allow a device associated with processingdevice 202 to execute multiple processes simultaneously. It isappreciated that other types of processor arrangements could beimplemented to provide the capabilities disclosed herein.

Consistent with the present disclosure, the methods and processesdisclosed herein may be performed by server 135 as a result ofprocessing device 202 executing one or more sequences of one or moreinstructions contained in a non-transitory computer-readable storagemedium. As used herein, a non-transitory computer-readable storagemedium refers to any type of physical memory on which information ordata readable by at least one processor may be stored. Examples includerandom access memory (RAM), read-only memory (ROM), volatile memory,nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, anyother optical data storage medium, any physical medium with patterns ofholes, a RAM, a PROM, an EPROM, a FLASH-EPROM or any other flash memory,NVRAM, a cache, a register, any other memory chip or cartridge, andnetworked versions of the same. The terms “memory” and“computer-readable storage medium” may refer to multiple structures,such as a plurality of memories or computer-readable storage mediumslocated within server 135, or at a remote location. Additionally, one ormore computer-readable storage mediums may be utilized in implementing acomputer-implemented method. The term “computer-readable storage medium”should be understood to include tangible items and exclude carrier wavesand transient signals.

According to one embodiment, server 135 may include network interface206 (which may also be any communications interface) coupled to bus 200.Network interface 206 may provide one-way or two-way data communicationto a local network, such as network 150. Network interface 206 mayinclude an integrated services digital network (ISDN) card, cable modem,satellite modem, or a modem to provide a data communication connectionto a corresponding type of telephone line. As another example, networkinterface 206 may include a local area network (LAN) card to provide adata communication connection to a compatible LAN. In anotherembodiment, network interface 206 may include an Ethernet port connectedto radio frequency receivers and transmitters and/or optical (e.g.,infrared) receivers and transmitters. The specific design andimplementation of network interface 206 depends on the communicationsnetwork(s) over which server 135 is intended to operate. As describedabove, server 135 may be a cloud server or a local server associatedwith retail store 105. In any such implementation, network interface 206may be configured to send and receive electrical, electromagnetic, oroptical signals, through wires or wirelessly, that may carry analog ordigital data streams representing various types of information. Inanother example, the implementation of network interface 206 may besimilar or identical to the implementation described below for networkinterface 306.

Server 135 may also include peripherals interface 208 coupled to bus200. Peripherals interface 208 may be connected to sensors, devices, andsubsystems to facilitate multiple functionalities. In one embodiment,peripherals interface 208 may be connected to I/O system 210 configuredto receive signals or input from devices and provide signals or outputto one or more devices that allow data to be received and/or transmittedby server 135. In one embodiment I/O system 210 may include or beassociated with output device 145. For example, I/O system 210 mayinclude a touch screen controller 212, an audio controller 214, and/orother input controller(s) 216. Touch screen controller 212 may becoupled to a touch screen 218. Touch screen 218 and touch screencontroller 212 can, for example, detect contact, movement, or breakthereof using any of a plurality of touch sensitivity technologies,including but not limited to capacitive, resistive, infrared, andsurface acoustic wave technologies as well as other proximity sensorarrays or other elements for determining one or more points of contactwith touch screen 218. Touch screen 218 may also, for example, be usedto implement virtual or soft buttons and/or a keyboard. In addition toor instead of touch screen 218, I/O system 210 may include a displayscreen (e.g., CRT, LCD, etc.), virtual reality device, augmented realitydevice, and so forth. Specifically, touch screen controller 212 (ordisplay screen controller) and touch screen 218 (or any of thealternatives mentioned above) may facilitate visual output from server135. Audio controller 214 may be coupled to a microphone 220 and aspeaker 222 to facilitate voice-enabled functions, such as voicerecognition, voice replication, digital recording, and telephonyfunctions. Specifically, audio controller 214 and speaker 222 mayfacilitate audio output from server 135. The other input controller(s)216 may be coupled to other input/control devices 224, such as one ormore buttons, keyboards, rocker switches, thumb-wheel, infrared port,USB port, image sensors, motion sensors, depth sensors, and/or a pointerdevice such as a computer mouse or a stylus.

In some embodiments, processing device 202 may use memory interface 204to access data and a software product stored on a memory device 226.Memory device 226 may include operating system programs for server 135that perform operating system functions when executed by the processingdevice. By way of example, the operating system programs may includeMicrosoft Windows™, Unix™ Linux™, Apple™ operating systems, personaldigital assistant (PDA) type operating systems such as Apple iOS, GoogleAndroid, Blackberry OS, or other types of operating systems.

Memory device 226 may also store communication instructions 228 tofacilitate communicating with one or more additional devices (e.g.,capturing device 125), one or more computers (e.g., output devices145A-145D) and/or one or more servers. Memory device 226 may includegraphical user interface instructions 230 to facilitate graphic userinterface processing; image processing instructions 232 to facilitateimage data processing-related processes and functions; sensor processinginstructions 234 to facilitate sensor-related processing and functions;web browsing instructions 236 to facilitate web browsing-relatedprocesses and functions; and other software instructions 238 tofacilitate other processes and functions. Each of the above identifiedinstructions and applications may correspond to a set of instructionsfor performing one or more functions described above. These instructionsneed not be implemented as separate software programs, procedures, ormodules. Memory device 226 may include additional instructions or fewerinstructions. Furthermore, various functions of server 135 may beimplemented in hardware and/or in software, including in one or moresignal processing and/or application specific integrated circuits. Forexample, server 135 may execute an image processing algorithm toidentify in received images one or more products and/or obstacles, suchas shopping carts, people, and more.

In one embodiment, memory device 226 may store database 140. Database140 may include product type model data 240 (e.g., an imagerepresentation, a list of features, a model obtained by training machinelearning algorithm using training examples, an artificial neuralnetwork, and more) that may be used to identify products in receivedimages; contract-related data 242 (e.g., planograms, promotions data,etc.) that may be used to determine if the placement of products on thestore shelves and/or the promotion execution are consistent withobligations of retail store 105; catalog data 244 (e.g., retail storechain's catalog, retail store's master file, etc.) that may be used tocheck if all product types that should be offered in retail store 105are in fact in the store, if the correct price is displayed next to anidentified product, etc.; inventory data 246 that may be used todetermine if additional products should be ordered from suppliers 115;employee data 248 (e.g., attendance data, records of training provided,evaluation and other performance-related communications, productivityinformation, etc.) that inay be used to assign specific store associatesto certain tasks; and calendar data 250 (e.g., holidays, national days,international events, etc.) that may be used to determine if a possiblechange in a product model is associated with a certain event. In otherembodiments of the disclosure, database 140 may store additional typesof data or fewer types of data. Furthermore, various types of data maybe stored in one or more memory devices other than memory device 226.Throughout this disclosure, the term store associate of a retail storemay refer to any person or a robot who is tasked with performing actionsin the retail store configured to support the operation of the retailstore. Some non-limiting examples of store associates may include storeemployees, subcontractors contracted to perform such actions in theretail store, employees of entities associated with the retail store(such as suppliers of the retail store, distributers of products sold inthe retail store, etc.), people engaged through crowd sourcing toperform such actions in the retail store, robots used to perform suchactions in the retail store, and so forth.

The components and arrangements shown in FIG. 2 are not intended tolimit the disclosed embodiments. As will be appreciated by a personskilled in the art having the benefit of this disclosure, numerousvariations and/or modifications may be made to the depictedconfiguration of server 135. For example, not all components may beessential for the operation of server 135 in all cases. Any componentmay be located in any appropriate part of server 135, and the componentsmay be rearranged into a variety of configurations while providing thefunctionality of the disclosed embodiments. For example, some serversmay not include some of the elements shown in I/O system 215.

FIG. 3 is a block diagram representation of an example configuration ofcapturing device 125. In one embodiment, capturing device 125 mayinclude a processing device 302, a memory interface 304, a networkinterface 306, and a peripherals interface 308 connected to image sensor310. These components may be separated or may be integrated in one ormore integrated circuits. The various components in capturing device 125may be coupled by one or more communication buses or signal lines (e.g.,bus 300). Different aspects of the functionalities of the variouscomponents in capturing device 125 may be understood from thedescription above regarding components of server 135 having similarfunctionality.

According to one embodiment, network interface 306 may be used tofacilitate communication with server 135. Network interface 306 may bean Ethernet port connected to radio frequency receivers and transmittersand/or optical receivers and transmitters. The specific design andimplementation of network interface 306 depends on the communicationsnetwork(s) over which capturing device 125 is intended to operate. Forexample, in some embodiments, capturing device 125 may include a networkinterface 306 designed to operate over a GSM network, a GPRS network, anEDGE network, a Wi-Fi or WiMax network, a Bluetooth® network, etc. Inanother example, the implementation of network interface 306 may besimilar or identical to the implementation described above for networkinterface 206.

In the example illustrated in FIG. 3 , peripherals interface 308 ofcapturing device 125 may be connected to at least one image sensor 310associated with at least one lens 312 for capturing image data in anassociated field of view. In some configurations, capturing device 125may include a plurality of image sensors associated with a plurality oflenses 312. In other configurations, image sensor 310 may be part of acamera included in capturing device 125. According to some embodiments,peripherals interface 308 may also be connected to other sensors (notshown), such as a motion sensor, a light sensor, infrared sensor, soundsensor, a proximity sensor, a temperature sensor, a biometric sensor, orother sensing devices to facilitate related functionalities. Inaddition, a positioning sensor may also be integrated with, or connectedto, capturing device 125. For example, such positioning sensor may beimplemented using one of the following technologies: Global PositioningSystem (GPS), GLObal NAvigation Satellite System (GLONASS), Galileoglobal navigation system, BeiDou navigation system, other GlobalNavigation Satellite Systems (GNSS), Indian Regional NavigationSatellite System (IRNSS), Local Positioning Systems (LPS), Real-TimeLocation Systems (RTLS), Indoor Positioning System (IPS), Wi-Fi basedpositioning systems, cellular triangulation, and so forth. For example,the positioning sensor may be built into mobile capturing device 125,such as smartphone devices. In another example, position software mayallow mobile capturing devices to use internal or external positioningsensors (e.g., connecting via a serial port or Bluetooth).

Consistent with the present disclosure, capturing device 125 may includedigital components that collect data from image sensor 310, transform itinto an image, and store the image on a memory device 314 and/ortransmit the image using network interface 306. In one embodiment,capturing device 125 may be fixedly mountable to a store shelf or toother objects in the retail store (such as walls, ceilings, floors,refrigerators, checkout stations, displays, dispensers, rods which maybe connected to other objects in the retail store, and so forth). In oneembodiment, capturing device 125 may be split into at least two housingssuch that only image sensor 310 and lens 312 may be visible on the storeshelf, and the rest of the digital components may be located in aseparate housing. An example of this type of capturing device isdescribed below with reference to FIGS. 5-7 .

Consistent with the present disclosure, capturing device 125 may usememory interface 304 to access memory device 314. Memory device 314 mayinclude high-speed, random access memory and/or non-volatile memory suchas one or more magnetic disk storage devices, one or more opticalstorage devices, and/or flash memory (e.g., NAND, NOR) to store capturedimage data. Memory device 314 may store operating system instructions316, such as DARWIN, RTXC, LINUX, iOS, UNIX, LINUX, OS X, WINDOWS, or anembedded operating system such as VXWorkS. Operating system 316 mayinclude instructions for handling basic system services and forperforming hardware dependent tasks. In some implementations, operatingsystem 316 may include a kernel (e.g., UNIX kernel, LINUX kernel, etc.).In addition, memory device 314 may store capturing instructions 318 tofacilitate processes and functions related to image sensor 310;graphical user interface instructions 320 that enables a user associatedwith capturing device 125 to control the capturing device and/or toacquire images of an area-of-interest in a retail establishment; andapplication instructions 322 to facilitate a process for monitoringcompliance of product placement or other processes.

The components and arrangements shown in FIG. 3 are not intended tolimit the disclosed embodiments. As will be appreciated by a personskilled in the art having the benefit of this disclosure, numerousvariations and/or modifications may be made to the depictedconfiguration of capturing device 125. For example, not all componentsare essential for the operation of capturing device 125 in all cases.Any component may be located in any appropriate part of capturing device125, and the components may be rearranged into a variety ofconfigurations while providing the functionality of the disclosedembodiments. For example, some capturing devices may not have lenses,and other capturing devices may include an external memory deviceinstead of memory device 314.

FIGS. 4A-4C illustrate example configurations for capturing image datain retail store 105 according to disclosed embodiments. FIG. 4Aillustrates how an aisle 400 of retail store 105 may be imaged using aplurality of capturing devices 125 fixedly connected to store shelves.FIG. 4B illustrates how aisle 400 of retail store 105 may be imagedusing a handheld communication device. FIG. 4C illustrates how aisle 400of retail store 105 may be imaged by robotic devices equipped withcameras.

With reference to FIG. 4A and consistent with the present disclosure,retail store 105 may include a plurality of capturing devices 125fixedly mounted (for example, to store shelves, walls, ceilings, floors,refrigerators, checkout stations, displays, dispensers, rods which maybe connected to other objects in the retail store, and so forth) andconfigured to collect image data. As depicted, one side of an aisle 400may include a plurality of capturing devices 125 (e.g., 125A, 125B, and125C) fixedly mounted thereon and directed such that they may captureimages of an opposing side of aisle 400. The plurality of capturingdevices 125 may be connected to an associated mobile power source (e.g.,one or more batteries), to an external power supply (e.g., a powergrid), obtain electrical power from a wireless power transmissionsystem, and so forth. As depicted in FIG. 4A, the plurality of capturingdevices 125 may be placed at different heights and at least theirvertical fields of view may be adjustable. Generally, both sides ofaisle 400 may include capturing devices 125 in order to cover both sidesof aisle 400.

Differing numbers of capturing devices 125 may be used to cover shelvingunit 402. In addition, there may be an overlap region in the horizontalfield of views of some of capturing devices 125. For example, thehorizontal fields of view of capturing devices (e.g., adjacent capturingdevices) may at least partially overlap with one another. In anotherexample, one capturing device may have a lower field of view than thefield of view of a second capturing device, and the two capturingdevices may have at least partially overlapping fields of view.According to one embodiment, each capturing device 125 may be equippedwith network interface 306 for communicating with server 135. In oneembodiment, the plurality of capturing devices 125 in retail store 105may be connected to server 135 via a single WLAN. Network interface 306may transmit information associated with a plurality of images capturedby the plurality of capturing devices 125 for analysis purposes. In oneexample, server 135 may determine an existence of an occlusion event(such as, by a person, by store equipment, such as a ladder, cart, etc.)and may provide a notification to resolve the occlusion event. Inanother example, server 135 may determine if a disparity exists betweenat least one contractual obligation and product placement as determinedbased on automatic analysis of the plurality of images. The transmittedinformation may include raw images, cropped images, processed imagedata, data about products identified in the images, and so forth.Network interface 306 may also transmit information identifying thelocation of the plurality capturing devices 125 in retail store 105.

With reference to FIG. 4B and consistent with the present disclosure,server 135 may receive image data captured by users 120. In a firstembodiment, server 135 may receive image data acquired by storeassociates. In one implementation, a handheld device of a storeassociate (e.g., capturing device 125D) may display a real-time videostream captured by the image sensor of the handheld device. Thereal-time video stream may be augmented with markings identifying to thestore associate an area-of-interest that needs manual capturing ofimages. One of the situations in which manual image capture may bedesirable may occur where the area-of-interest is outside the fields ofview of a plurality of cameras fixedly connected to store shelves inaisle 400. In other situations, manual capturing of images of anarea-of-interest may be desirable when a current set of acquired imagesis out of date (e.g., obsolete in at least one respect) or of poorquality (e.g., lacking focus, obstacles, lesser resolution, lack oflight, etc.). Additional details of this embodiment are described inApplicant's International Patent Application No. PCT/IB2018/001107,which is incorporated herein by reference.

In a second embodiment, server 135 may receive image data acquired bycrowd sourcing. In one exemplary implementation, server 135 may providea request to a detected mobile device for an updated image of thearea-of-interest in aisle 400. The request may include an incentive(e.g., $2 discount) to user 120 for acquiring the image. In response tothe request, user 120 may acquire and transmit an up-to-date image ofthe area-of-interest. After receiving the image from user 120, server135 may transmit the accepted incentive or agreed upon reward to user120. The incentive may comprise a text notification and a redeemablecoupon. In some embodiments, the incentive may include a redeemablecoupon for a product associated with the area-of-interest. Server 135may generate image-related data based on aggregation of data from imagesreceived from crowd sourcing and from images received from a pluralityof cameras fixedly connected to store shelves. Additional details ofthis embodiment are described in Applicant's International PatentApplication No. PCT/IB2017/000919, which is incorporated herein byreference.

With reference to FIG. 4C and consistent with the present disclosure,server 135 may receive image data captured by robotic devices withcameras traversing in aisle 400. The present disclosure is not limitedto the type of robotic devices used to capture images of retail store105. In some embodiments, the robotic devices may include a robot on atrack (e.g., a Cartesian robot configured to move along an edge of ashelf or in parallel to a shelf, such as capturing device 125E), a drone(e.g., capturing device 125F), and/or a robot that may move on the floorof the retail store (e.g., a wheeled robot such as capturing device125G, a legged robot, a snake-like robot, etc.). The robotic devices maybe controlled by server 135 and may be operated remotely orautonomously. In one example, server 135 may instruct capturing device125E to perform periodic scans at times when no customers or otherobstructions are identified in aisle 400. Specifically, capturing device125E may be configured to move along store shelf 404 and to captureimages of products placed on store shelf 404, products placed on storeshelf 406, or products located on shelves opposite store shelf (e.g.,store shelf 408). In another example, server 135 may instruct capturingdevice 125F to perform a scan of all the area of retail store 105 beforethe opening hour. In another example, server 135 may instruct capturingdevice 125G to capture a specific area-of-interest, similar as describedabove with reference to receiving images acquired by the storeassociates. In some embodiments, robotic capturing devices (such as 125Fand 125G) may include an internal processing unit that may allow them tonavigate autonomously within retail store 105. For example, the roboticcapturing devices may use input from sensors (e.g., image sensors, depthsensors, proximity sensors, etc.), to avoid collision with objects orpeople, and to complete the scan of the desired area of retail store105.

As discussed above with reference to FIG. 4A, the image datarepresentative of products displayed on store shelves may be acquired bya plurality of stationary capturing devices 125 fixedly mounted in theretail store. One advantage of having stationary image capturing devicesspread throughout retail store 105 is the potential for acquiringproduct images from set locations and on an ongoing basis such thatup-to-date product status may be determined for products throughout aretail store at any desired periodicity (e.g., in contrast to a movingcamera system that may acquire product images more infrequently).However, there may be certain challenges in this approach. The distancesand angles of the image capturing devices relative to the capturedproducts should be selected such as to enable adequate productidentification, especially when considered in view of image sensorresolution and/or optics specifications. For example, a capturing deviceplaced on the ceiling of retail store 105 may have sufficientresolutions and optics to enable identification of large products (e.g.,a pack of toilet paper), but may be insufficient for identifying smallerproducts (e.g., deodorant packages). The image capturing devices shouldnot occupy shelf space that is reserved for products for sale. The imagecapturing devices should not be positioned in places where there is alikelihood that their fields of view will be regularly blocked bydifferent objects. The image capturing devices should be able tofunction for long periods of time with minimum maintenance. For example,a requirement for frequent replacement of batteries may render certainimage acquisition systems cumbersome to use, especially where many imageacquisition devices are in use throughout multiple locations in a retailstore and across multiple retail stores. The image capturing devicesshould also include processing capabilities and transmissioncapabilities for providing real time or near real time image data aboutproducts. The disclosed image acquisition systems address thesechallenges.

FIG. 5A illustrates an example of a system 500 for acquiring images ofproducts in retail store 105. Throughout the disclosure, capturingdevice 125 may refer to a system, such as system 500 shown in FIG. 5A.As shown, system 500 may include a first housing 502 configured forlocation on a retail shelving unit (e.g., as illustrated in FIG. 5B),and a second housing 504 configured for location on the retail shelvingunit separate from first housing 502. The first and the second housingmay be configured for mounting on the retail shelving unit in anysuitable way (e.g., screws, bolts, clamps, adhesives, magnets,mechanical means, chemical means, etc.). In some embodiments, firsthousing 502 may include an image capture device 506 (e.g., a cameramodule that may include image sensor 310) and second housing 504 mayinclude at least one processor (e.g., processing device 302) configuredto control image capture device 506 and also to control a networkinterface (e.g., network interface 306) for communicating with a remoteserver (e.g., server 135).

System 500 may also include a data conduit 508 extending between firsthousing 502 and second housing 504. Data conduit 508 may be configuredto enable transfer of control signals from the at least one processor toimage capture device 506 and to enable collection of image data acquiredby image capture device 506 for transmission by the network interface.Consistent with the present disclosure, the term “data conduit” mayrefer to a communications channel that may include either a physicaltransmission medium such as a wire or a logical connection over amultiplexed medium such as a radio channel In some embodiments, dataconduit 508 may be used for conveying image data from image capturedevice 506 to at least one processor located in second housing 504.Consistent with one implementation of system 500, data conduit 508 mayinclude flexible printed circuits and may have a length of at leastabout 5 cm, at least about 10 cm, at least about 15 cm, etc. The lengthof data conduit 508 may be adjustable to enable placement of firsthousing 502 separately from second housing 504. For example, in someembodiments, data conduit may be retractable within second housing 504such that the length of data conduit exposed between first housing 502and second housing 504 may be selectively adjusted.

In one embodiment, the length of data conduit 508 may enable firsthousing 502 to be mounted on a first side of a horizontal store shelffacing the aisle (e.g., store shelf 510 illustrated in FIG. 5B) andsecond housing 504 to be mounted on a second side of store shelf 510that faces the direction of the ground (e.g., an underside of a storeshelf). In this embodiment, data conduit 508 may be configured to bendaround an edge of store shelf 510 or otherwise adhere/follow contours ofthe shelving unit. For example, a first portion of data conduit 508 maybe configured for location on the first side of store shelf 510 (e.g., aside facing an opposing retail shelving unit across an aisle) and asecond portion of data conduit 508 may be configured for location on asecond side of store shelf 510 (e.g., an underside of the shelf, whichin some cases may be orthogonal to the first side). The second portionof data conduit 508 may be longer than the first portion of data conduit508. Consistent with another embodiment, data conduit 508 may beconfigured for location within an envelope of a store shelf. Forexample, the envelope may include the outer boundaries of a channellocated within a store shelf, a region on an underside of an L-shapedstore shelf, a region between two store shelves, etc. Consistent withanother implementation of system 500 discussed below, data conduit 508may include a virtual conduit associated with a wireless communicationslink between first housing 502 and second housing 504.

FIG. 5B illustrates an exemplary configuration for mounting firsthousing 502 on store shelf 510. Consistent with the present disclosure,first housing 502 may be placed on store shelf 510, next to or embeddedin a plastic cover that may be used for displaying prices.Alternatively, first housing 502 may be placed or mounted on any otherlocation in retail store 105. For example, first housing 502 may beplaced or mounted on the walls, on the ceiling, on refrigerator units,on display units, and more. The location and/or orientation of firsthousing 502 may be selected such that a field of view of image capturedevice 506 may cover at least a portion of an opposing retail shelvingunit. Consistent with the present disclosure, image capture device 506may have a view angle of between 50 and 80 degrees, about 62 degrees,about 67 degrees, or about 75 degrees. Consistent with the presentdisclosure, image capture device 506 may include an image sensor havingsufficient image resolution to enable detection of text associated withlabels on an opposing retail shelving unit. In one embodiment, the imagesensor may include m*n pixels. For example, image capture device 506 mayhave an 8MP image sensor that includes an array of 3280*2464 pixels.Each pixel may include at least one photo-voltaic cell that converts thephotons of the incident light to an electric signal. The electricalsignal may be converted to digital data by an A/D converter andprocessed by the image processor (ISP). In one embodiment, the imagesensor of image capture device 506 may be associated with a pixel sizeof between 1.1×1.1 um² and 1.7×1.7 um², for example, 1.4×1.4 um².

Consistent with the present disclosure, image capture device 506 may beassociated with a lens (e.g., lens 312) having a fixed focal lengthselected according to a distance expected to be encountered betweenretail shelving units on opposite sides of an aisle (e.g., distance d1shown in FIG. 6A) and/or according to a distance expected to beencountered between a side of a shelving unit facing the aisle on oneside of an aisle and a side of a shelving unit facing away of the aisleon the other side of the aisle (e.g., distance d2 shown in FIG. 6A). Thefocal length may also be based on any other expected distance betweenthe image acquisition device and products to be imaged. As used herein,the term “focal length” refers to the distance from the optical centerof the lens to a point where objects located at the point aresubstantially brought into focus. In contrast to zoom lenses, in fixedlenses the focus is not adjustable. The focus is typically set at thetime of lens design and remains fixed. In one embodiment, the focallength of lens 312 may be selected based on the distance between twosides of aisles in the retail store (e.g., distance d1, distance d2, andso forth). In some embodiments, image capture device 506 may include alens with a fixed focal length having a fixed value between 2.5 mm and4.5 mm, such as about 3.1 mm, about 3.4 mm, about 3.7 mm. For example,when distance d1 between two opposing retail shelving units is about 2meters, the focal length of the lens may be about 3.6 mm. Unlessindicated otherwise, the term “about” with regards to a numeric value isdefined as a variance of up to 5% with respect to the stated value. Ofcourse, image capture devices having non-fixed focal lengths may also beused depending on the requirements of certain imaging environments, thepower and space resources available, etc.

FIG. 5C illustrates an exploded view of second housing 504. In someembodiments, the network interface located in second housing 504 (e.g.,network interface 306) may be configured to transmit to server 135information associated with a plurality of images captured by imagecapture device 506. For example, the transmitted information may be usedto determine if a disparity exists between at least one contractualobligation (e.g. planogram) and product placement. In one example, thenetwork interface may support transmission speeds of 0.5 Mb/s, 1 Mb/s,Mb/s, or more. Consistent with the present disclosure, the networkinterface may allow different modes of operations to be selected, suchas: high-speed, slope-control, or standby. In high-speed mode,associated output drivers may have fast output rise and fall times tosupport high-speed bus rates; in slope-control, the electromagneticinterference may be reduced and the slope (i.e., the change of voltageper unit of time) may be proportional to the current output; and instandby mode, the transmitter may be switched off and the receiver mayoperate at a lower current.

Consistent with the present disclosure, second housing 504 may include apower port 512 for conveying energy from a power source to first housing502. In one embodiment, second housing 504 may include a section for atleast one mobile power source 514 (e.g., in the depicted configurationthe section is configured to house four batteries). The at least onemobile power source may provide sufficient power to enable image capturedevice 506 to acquire more than 1,000 pictures, more than 5,000pictures, more than 10,000 pictures, or more than 15,000 pictures, andto transmit them to server 135. In one embodiment, mobile power source514 located in a single second housing 504 may power two or more imagecapture devices 506 mounted on the store shelf. For example, as depictedin FIGS. 6A and 6B, a single second housing 504 may be connected to aplurality of first housings 502 with a plurality of image capturedevices 506 covering different (overlapping or non-overlapping) fieldsof view. Accordingly, the two or more image capture devices 506 may bepowered by a single mobile power source 514 and/or the data captured bytwo or more image capture devices 506 may be processed to generate apanoramic image by a single processing device located in second housing504. In addition to mobile power source 514 or as an alternative tomobile power source 514, second housing 504 may also be connected to anexternal power source. For example, second housing 504 may be mounted toa store shelf and connected to an electric power grid. In this example,power port 512 may be connected to the store shelf through a wire forproviding electrical power to image capture device 506. In anotherexample, a retail shelving unit or retail store 105 may include awireless power transmission system, and power port 512 may be connectedto a device configured to obtain electrical power from the wirelesspower transmission system. In addition, as discussed below, system 500may use power management policies to reduce the power consumption. Forexample, system 500 may use selective image capturing and/or selectivetransmission of images to reduce the power consumption or conservepower.

FIG. 6A illustrates a schematic diagram of a top view of aisle 600 inretail store 105 with multiple image acquisition systems 500 (e.g.,500A, 500B, 500C, 500D, and 500E) deployed thereon for acquiring imagesof products. Aisle 600 may include a first retail shelving unit 602 anda second retail shelving unit 604 that opposes first retail shelvingunit 602. In some embodiments, different numbers of systems 500 may bemounted on opposing retail shelving units. For example, system 500A(including first housing 502A, second housing 504A, and data conduit508A), system 500B (including first housing 502B second housing 504B,and data conduit 508B), and system 500C (including first housing 502C,second housing 504C, and data conduit 508C) may be mounted on firstretail shelving unit 602; and system 500D (including first housing502D1, first housing 502D2, second housing 504D, and data conduits 508D1and 508D2) and system 500E (including first housing 502E1, first housing502E2, second housing 504E, and data conduits 508E1 and 508E2) may bemounted on second retail shelving unit 604. Consistent with the presentdisclosure, image capture device 506 may be configured relative to firsthousing 502 such that an optical axis of image capture device 506 isdirected toward an opposing retail shelving unit when first housing 502is fixedly mounted on a retail shelving unit. For example, optical axis606 of the image capture device associated with first housing 502B maybe directed towards second retail shelving unit 604 when first housing502B is fixedly mounted on first retail shelving unit 602. A singleretail shelving unit may hold a number of systems 500 that include aplurality of image capturing devices. Each of the image capturingdevices may be associated with a different field of view directed towardthe opposing retail shelving unit. Different vantage points ofdifferently located image capture devices may enable image acquisitionrelative to different sections of a retail shelf. For example, at leastsome of the plurality of image capturing devices may be fixedly mountedon shelves at different heights. Examples of such a deployment areillustrated in FIGS. 4A and 6B.

As shown in FIG. 6A each first housing 502 may be associated with a dataconduit 508 that enables exchanging of information (e.g., image data,control signals, etc.) between the at least one processor located insecond housing 504 and image capture device 506 located in first housing502. In some embodiments, data conduit 508 may include a wiredconnection that supports data-transfer and may be used to power imagecapture device 506 (e.g., data conduit 508A, data conduit 508B, dataconduit 508D1, data conduit 508D2, data conduit 508E1, and data conduit508E2). Consistent with these embodiments, data conduit 508 may complywith a wired standard such as USB, Micro-USB, HDMI, Micro-HDMI,Firewire, Apple, etc. In other embodiments, data conduit 508 may be awireless connection, such as a dedicated communications channel betweenthe at least one processor located in second housing 504 and imagecapture device 506 located in first housing 502 (e.g., data conduit508C). In one example, the communications channel may be established bytwo Ncar Field Communication (NFC) transceivers. In other examples,first housing 502 and second housing 504 may include interface circuitsthat comply with other short-range wireless standards such as Bluetooth,WiFi, ZigBee, etc.

In some embodiments of the disclosure, the at least one processor ofsystem 500 may cause at least one image capture device 506 toperiodically capture images of products located on an opposing retailshelving unit (e.g., images of products located on a shelf across anaisle from the shelf on which first housing 502 is mounted). The term“periodically capturing images” includes capturing an image or images atpredetermined time intervals (e.g., every minute, every 30 minutes,every 150 minutes, every 300 minutes, etc.), capturing video, capturingan image every time a status request is received, and/or capturing animage subsequent to receiving input from an additional sensor, forexample, an associated proximity sensor. Images may also be capturedbased on various other triggers or in response to various other detectedevents. In some embodiments, system 500 may receive an output signalfrom at least one sensor located on an opposing retail shelving unit.For example, system 500B may receive output signals from a sensingsystem located on second retail shelving unit 604. The output signalsmay be indicative of a sensed lifting of a product from second retailshelving unit 604 or a sensed positioning of a product on second retailshelving unit 604. In response to receiving the output signal from theat least one sensor located on second retail shelving unit 604, system500B may cause image capture device 506 to capture one or more images ofsecond retail shelving unit 604. Additional details on a sensing system,including the at least one sensor that generates output signalsindicative of a sensed lifting of a product from an opposing retailshelving unit, is discussed below with reference to FIGS. 8-10 .

Consistent with embodiments of the disclosure, system 500 may detect anobject 608 in a selected area between first retail shelving unit 602 andsecond retail shelving unit 604. Such detection may be based on theoutput of one or more dedicated sensors (e.g., motion detectors, etc.)and/or may be based on image analysis of one or more images acquired byan image acquisition device. Such images, for example, may include arepresentation of a person or other object recognizable through variousimage analysis techniques (e.g., trained neural networks, Fouriertransform analysis, edge detection, filters, face recognition, etc.).The selected area may be associated with distance d1 between firstretail shelving unit 602 and second retail shelving unit 604. Theselected area may be within the field of view of image capture device506 or an area where the object causes an occlusion of a region ofinterest (such as a shelf, a portion of a shelf being monitored, andmore). Upon detecting object 608, system 500 may cause image capturedevice 506 to forgo image acquisition while object 608 is within theselected area. In one example, object 608 may be an individual, such asa customer or a store associate. In another example, detected object 608may be an inanimate object, such as a cart, box, carton, one or moreproducts, cleaning robots, etc. In the example illustrated in FIG. 6A,system 500A may detect that object 608 has entered into its associatedfield of view (e.g., using a proximity sensor) and may instruct imagecapturing device 506 to forgo image acquisition. In alternativeembodiments, system 500 may analyze a plurality of images acquired byimage capture device 506 and identify at least one image of theplurality of images that includes a representation of object 608.Thereafter, system 500 may avoid transmission of at least part of the atleast one identified image and/or information based on the at least oneidentified image to server 135.

As shown in FIG. 6A, the at least one processor contained in a secondhousing 504 may control a plurality of image capture devices 506contained in a plurality of first housings 502 (e.g., systems 500D and500E). Controlling image capturing device 506 may include instructingimage capturing device 506 to capture an image and/or transmit capturedimages to a remote server (e.g., server 135). In some cases, each of theplurality of image capture devices 506 may have a field of view that atleast partially overlaps with a field of view of at least one otherimage capture device 506 from among plurality of image capture devices506. In one embodiment, the plurality of image capture devices 506 maybe configured for location on one or more horizontal shelves and may bedirected to substantially different areas of the opposing first retailshelving unit. In this embodiment, the at least one processor maycontrol the plurality of image capture devices such that each of theplurality of image capture devices may capture an image at a differenttime. For example, system 500E may have a second housing 504E with atleast one processor that may instruct a first image capturing devicecontained in first housing 502E1 to capture an image at a first time andmay instruct a second image capturing device contained in first housing502E2 to capture an image at a second time which differs from the firsttime. Capturing images in different times (or forwarding them to the atleast one processor at different times) may assist in processing theimages and writing the images in the memory associated with the at leastone processor.

FIG. 6B illustrates a perspective view assembly diagram depicting aportion of a retail shelving unit 620 with multiple systems 500 (e.g.,500F, 500G, 500H, 500I, and 500J) deployed thereon for acquiring imagesof products. Retail shelving unit 620 may include horizontal shelves atdifferent heights. For example, horizontal shelves 622A, 622B, and 622Care located below horizontal shelves 622D, 622E, and 622F. In someembodiments, a different number of systems 500 may be mounted on shelvesat different heights. For example, system 500F (including first housing502F and second housing 504F), system 500G (including first housing 502Gand second housing 504G), and system 500H (including first housing 502Hand second housing 504H) may be mounted on horizontal shelves associatedwith a first height; and system 500I (including first housing 502I,second housing 504I, and a projector 632) and system 500J (includingfirst housing 502J1, first housing 502J2, and second housing 504J) maybe mounted on horizontal shelves associated with a second height. Insome embodiments, retail shelving unit 620 may include a horizontalshelf with at least one designated place (not shown) for mounting ahousing of image capturing device 506. The at least one designated placemay be associated with connectors such that first housing 502 may befixedly mounted on a side of horizontal shelf 622 facing an opposingretail shelving unit using the connectors.

Consistent with the present disclosure, system 500 may be mounted on aretail shelving unit that includes at least two adjacent horizontalshelves (e.g., shelves 622A and 622B) forming a substantially continuoussurface for product placement. The store shelves may include standardstore shelves or customized store shelves. A length of each store shelf622 may be at least 50 cm, less than 200 cm, or between 75 cm to 175 cm.In one embodiment, first housing 502 may be fixedly mounted on theretail shelving unit in a slit between two adjacent horizontal shelves.For example, first housing 502G may be fixedly mounted on retailshelving unit 620 in a slit between horizontal shelf 622B and horizontalshelf 622C. In another embodiment, first housing 502 may be fixedlymounted on a first shelf and second housing 504 may be fixedly mountedon a second shelf. For example, first housing 502I may be mounted onhorizontal shelf 622D and second housing 504I may be mounted onhorizontal shelf 622E. In another embodiment, first housing 502 may befixedly mounted on a retail shelving unit on a first side of ahorizontal shelf facing the opposing retail shelving unit and secondhousing 504 may be fixedly mounted on retail shelving unit 620 on asecond side of the horizontal shelf orthogonal to the first side. Forexample, first housing 502H may mounted on a first side 624 ofhorizontal shelf 622C next to a label and second housing 504H may bemounted on a second side 626 of horizontal shelf 622C that faces down(e.g., towards the ground or towards a lower shelf). In anotherembodiment, second housing 504 may be mounted closer to the back of thehorizontal shelf than to the front of the horizontal shelf. For example,second housing 504H may be fixedly mounted on horizontal shelf 622C onsecond side 626 closer to third side 628 of the horizontal shelf 622Cthan to first side 624. Third side 628 may be parallel to first side624. As mentioned above, data conduit 508 (e.g., data conduit 508H) mayhave an adjustable or selectable length for extending between firsthousing 502 and second housing 504. In one embodiment, when firsthousing 502H is fixedly mounted on first side 624, the length of dataconduit 508H may enable second housing 604H to be fixedly mounted onsecond side 626 closer to third side 628 than to first side 624.

As mentioned above, at least one processor contained in a single secondhousing 504 may control a plurality of image capture devices 506contained in a plurality of first housings 502 (e.g., system 500J). Insome embodiments, the plurality of image capture devices 506 may beconfigured for location on a single horizontal shelf and may be directedto substantially the same area of the opposing first retail shelvingunit (e.g., system 500D in FIG. 6A). In these embodiments, the imagedata acquired by the first image capture device and the second imagecapture device may enable a calculation of depth information (e.g.,based on image parallax information) associated with at least oneproduct positioned on an opposing retail shelving unit. For example,system 500J may have single second housing 504J with at least oneprocessor that may control a first image capturing device contained infirst housing 502J1 and a second image capturing device contained infirst housing 502J2. The distance d3 between the first image capturedevice contained in first housing 502J1 and the second image capturedevice contained in first housing 502J2 may be selected based on thedistance between retail shelving unit 620 and the opposing retailshelving unit (e.g., similar to d1 and/or d2). For example, distance d3may be at least 5 cm, at least 10 cm, at least 15 cm, less than 40 cm,less than 30 cm, between about 5 cm to about 20 cm, or between about 10cm to about 15 cm. In another example, d3 may be a function of d1 and/ord2, a linear function of d1 and/or d2, a function of d1*log(d1) and/ord2*log(d2) such as a1*d1*log(d1) for some constant al, and so forth. Thedata from the first image capturing device contained in first housing502J1 and the second image capturing device contained in first housing502J2 may be used to estimate the number of products on a store shelf ofretail shelving unit 602. In related embodiments, system 500 may controla projector (e.g., projector 632) and image capture device 506 that areconfigured for location on a single store shelf or on two separate storeshelves. For example, projector 632 may be mounted on horizontal shelf622E and image capture device 506I may be mounted on horizontal shelf622D. The image data acquired by image capture device 506 (e.g.,included in first housing 502I) may include reflections of lightpatterns projected from projector 632 on the at least one product and/orthe opposing retail shelving unit and may enable a calculation of depthinformation associated with at least one product positioned on theopposing retail shelving unit. The distance between projector 632 andthe image capture device contained in first housing 502I may be selectedbased on the distance between retail shelving unit 620 and the opposingretail shelving unit (e.g., similar to d1 and/or d2). For example, thedistance between the projector and the image capture device may be atleast 5 cm, at least 10 cm, at least 15 cm, less than 40 cm, less than30 cm, between about 5 cm to about 20 cm, or between about 10 cm toabout 15 cm. In another example, the distance between the projector andthe image capture device may be a function of d1 and/or d2, a linearfunction of d1 and/or d2, a function of d1*log(d1) and/or d2*log(d2)such as a1*d1*log(d1) for some constant a1, and so forth.

Consistent with the present disclosure, a central communication device630 may be located in retail store 105 and may be configured tocommunicate with server 135 (e.g., via an Internet connection). Thecentral communication device may also communicate with a plurality ofsystems 500 (for example, less than ten, ten, eleven, twelve, more thantwelve, and so forth). In some cases, at least one system of theplurality of systems 500 may be located in proximity to centralcommunication device 630. In the illustrated example, system 500F may belocated in proximity to central communication device 630. In someembodiments, at least some of systems 500 may communicate directly withat least one other system 500. The communications between some of theplurality of systems 500 may happen via a wired connection, such as thecommunications between system 500J and system 500I and thecommunications between system 500H and system 500G. Additionally oralternatively, the communications between some of the plurality ofsystems 500 may occur via a wireless connection, such as thecommunications between system 500G and system 500F and thecommunications between system 500I and system 500F. In some examples, atleast one system 500 may be configured to transmit captured image data(or information derived from the captured image data) to centralcommunication device 630 via at least two mediating systems 500, atleast three mediating systems 500, at least four mediating systems 500,or more. For example, system 500J may convey captured image data tocentral communication device 630 via system 500I and system 500F.

Consistent with the present disclosure, two (or more) systems 500 mayshare information to improve image acquisition. For example, system 500Jmay be configured to receive from a neighboring system 500I informationassociated with an event that system 500I had identified, and controlimage capture device 506 based on the received information. For example,system 500J may forgo image acquisition based on an indication fromsystem 500I that an object has entered or is about to enter its field ofview. Systems 500I and 500J may have overlapping fields of view ornon-overlapping fields of view. In addition, system 500J may alsoreceive (from system 500I) information that originates from centralcommunication device 630 and control image capture device 506 based onthe received information. For example, system 500I may receiveinstructions from central communication device 630 to capture an imagewhen suppler 115 inquiries about a specific product that is placed in aretail unit opposing system 500I. In some embodiments, a plurality ofsystems 500 may communicate with central communication device 630. Inorder to reduce or avoid network congestion, each system 500 mayidentify an available transmission time slot. Thereafter, each system500 may determine a default time slot for future transmissions based onthe identified transmission time slot.

FIG. 6C provides a diagrammatic representation of a retail shelving unit640 being captured by multiple systems 500 (e.g., system 500K and system500L) deployed on an opposing retail shelving unit (not shown). FIG. 6Cillustrates embodiments associated with the process of installingsystems 500 in retail store 105. To facilitate the installation ofsystem 500, each first housing 502 (e.g., first housing 502K) mayinclude an adjustment mechanism 642 for setting a field of view 644 ofimage capture device 506K such that the field of view 644 will at leastpartially encompass products placed both on a bottom shelf of retailshelving unit 640 and on a top shelf of retail shelving unit 640. Forexample, adjustment mechanism 642 may enable setting the position ofimage capture device 506K relative to first housing 502K. Adjustmentmechanism 642 may have at least two degrees of freedom to separatelyadjust manually (or automatically) the vertical field of view and thehorizontal field of view of image capture device 506K. In oneembodiment, the angle of image capture device 506K may be measured usingposition sensors associated with adjustment mechanism 642, and themeasured orientation may be used to determine if image capture device506K is positioned in the right direction. In one example, the output ofthe position sensors may be displayed on a handheld device of a person(such as a store associate) installing image capturing device 506K. Suchan arrangement may provide the store associate/installer with real timevisual feedback representative of the field of view of an imageacquisition device being installed.

In addition to adjustment mechanism 642, first housing 502 may include afirst physical adapter (not shown) configured to operate with multipletypes of image capture device 506 and a second physical adapter (notshown) configured to operate with multiple types of lenses. Duringinstallation, the first physical adapter may be used to connect asuitable image capture device 506 to system 500 according to the levelof recognition requested (e.g., detecting a barcode from products,detecting text and price from labels, detecting different categories ofproducts, etc.). Similarly, during installation, the second physicaladapter may be used to associate a suitable lens to image capture device506 according to the physical conditions at the store (e.g., thedistance between the aisles, the horizontal field of view required fromimage capture device 506, and/or the vertical field of view requiredfrom image capture device 506). The second physical adapter provides thestore associate/installer the ability to select the focal length of lens312 during installation according to the distance between retailshelving units on opposite sides of an aisle (e.g., distance d1 and/ordistance d2 shown in FIG. 6A). In some embodiments, adjustment mechanism642 may include a locking mechanism to reduce the likelihood ofunintentional changes in the field of view of image capture device 506.Additionally or alternatively, the at least one processor contained insecond housing 504 may detect changes in the field of view of imagecapture device 506 and issue a warning when a change is detected, when achange larger than a selected threshold is detected, when a change isdetected for a duration longer than a selected threshold, and so forth.

In addition to adjustment mechanism 642 and the different physicaladapters, system 500 may modify the image data acquired by image capturedevice 506 based on at least one attribute associated with opposingretail shelving unit 640. Consistent with the present disclosure, the atleast one attribute associated with retail shelving unit 640 may includea lighting condition, the dimensions of opposing retail shelving unit640, the size of products displayed on opposing retail shelving unit640, the type of labels used on opposing retail shelving unit 640, andmore. In some embodiments, the attribute may be determined, based onanalysis of one or more acquired images, by at least one processorcontained in second housing 504. Alternatively, the attribute may beautomatically sensed and conveyed to the at least one processorcontained in second housing 504. In one example, the at least oneprocessor may change the brightness of captured images based on thedetected light conditions. In another example, the at least oneprocessor may modify the image data by cropping the image such that itwill include only the products on retail shelving unit (e.g., not toinclude the floor or the ceiling), only area of the shelving unitrelevant to a selected task (such as planogram compliance check), and soforth.

Consistent with the present disclosure, during installation, system 500may enable real-time display 646 of field of view 644 on a handhelddevice 648 of a user 650 installing image capturing device 506K. In oneembodiment, real-time display 646 of field of view 644 may includeaugmented markings 652 indicating a location of a field of view 654 ofan adjacent image capture device 506L. In another embodiment, real-timedisplay 646 of field of view 644 may include augmented markings 656indicating a region of interest in opposing retail shelving unit 640.The region of interest may be determined based on a planogram,identified product type, and/or part of retail shelving unit 640. Forexample, the region of interest may include products with a greaterlikelihood of planogram incompliance. In addition, system 500K mayanalyze acquired images to determine if field of view 644 includes thearea that image capturing device 506K is supposed to monitor (forexample, from labels on opposing retail shelving unit 640, products onopposing retail shelving unit 640, images captured from other imagecapturing devices that may capture other parts of opposing retailshelving unit 640 or capture the same part of opposing retail shelvingunit 640 but in a lower resolution or at a lower frequency, and soforth). In additional embodiments, system 500 may further comprise anindoor location sensor which may help determine if the system 500 ispositioned at the right location in retail store 105.

In some embodiments, an anti-theft device may be located in at least oneof first housing 502 and second housing 504. For example, the anti-theftdevice may include a specific RF label or a pin-tag radio-frequencyidentification device, which may be the same or similar to a type ofanti-theft device that is used by retail store 105 in which system 500is located. The RF label or the pin-tag may be incorporated within thebody of first housing 502 and second housing 504 and may not be visible.In another example, the anti theft device may include a motion sensorwhose output may be used to trigger an alarm in the case of motion ordisturbance, in case of motion that is above a selected threshold, andso forth.

FIG. 7A includes a flowchart representing an exemplary method 700 foracquiring images of products in retail store 105 in accordance withexample embodiments of the present disclosure. For purposes ofillustration, in the following description, reference is made to certaincomponents of system 500 as deployed in the configuration depicted inFIG. 6A. It will be appreciated, however, that other implementations arepossible and that other configurations may be utilized to implement theexemplary method. It will also be readily appreciated that theillustrated method may be altered to modify the order of steps, deletesteps, or further include additional steps.

At step 702, the method includes fixedly mounting on first retailshelving unit 602 at least one first housing 502 containing at least oneimage capture device 506 such that an optical axis (e.g., optical axis606) of at least one image capture device 506 is directed to secondretail shelving unit 604. In one embodiment, fixedly mounting firsthousing 502 on first retail shelving unit 602 may include placing firsthousing 502 on a side of store shelf 622 facing second retail shelvingunit 604. In another embodiment, fixedly mounting first housing 502 onretail shelving unit 602 may include placing first housing 502 in a slitbetween two adjacent horizontal shelves. In some embodiments, the methodmay further include fixedly mounting on first retail shelving unit 602at least one projector (such as projector 632) such that light patternsprojected by the at least one projector are directed to second retailshelving unit 604. In one embodiment, the method may include mountingthe at least one projector to first retail shelving unit 602 at aselected distance to first housing 502 with image capture device 506. Inone embodiment, the selected distance may be at least 5 cm, at least 10cm, at least 15 cm, less than 40 cm, less than 30 cm, between about 5 cmto about 20 cm, or between about 10 cm to about 15 cm. In oneembodiment, the selected distance may be calculated according to adistance between to first retail shelving unit 602 and second retailshelving unit 604, such as d1 and/or d2, for example selecting thedistance to be a function of d1 and/or d2, a linear function of d1and/or d2, a function of d1*log(d1) and/or d2*log(d2) such asa1*d1*log(d1) for some constant a1, and so forth.

At step 704, the method includes fixedly mounting on first retailshelving unit 602 second housing 504 at a location spaced apart from theat least one first housing 502, second housing 504 may include at leastone processor (e.g., processing device 302). In one embodiment, fixedlymounting second housing 504 on the retail shelving unit may includeplacing second housing 504 on a different side of store shelf 622 thanthe side first housing 502 is mounted on.

At step 706, the method includes extending at least one data conduit 508between at least one first housing 502 and second housing 504. In oneembodiment, extending at least one data conduit 508 between at least onefirst housing 502 and second housing 504 may include adjusting thelength of data conduit 508 to enable first housing 502 to be mountedseparately from second housing 504. At step 708, the method includescapturing images of second retail shelving unit 604 using at least oneimage capture device 506 contained in at least one first housing 502(e.g., first housing 502A, first housing 502B, or first housing 502C).In one embodiment, the method further includes periodically capturingimages of products located on second retail shelving unit 604. Inanother embodiment the method includes capturing images of second retailshelving unit 604 after receiving a trigger from at least one additionalsensor in communication with system 500 (wireless or wired).

At step 710, the method includes transmitting at least some of thecaptured images from second housing 504 to a remote server (e.g., server135) configured to determine planogram compliance relative to secondretail shelving unit 604. In some embodiments, determining planogramcompliance relative to second retail shelving unit 604 may includedetermining at least one characteristic of planogram compliance based ondetected differences between the at least one planogram and the actualplacement of the plurality of product types on second retail shelvingunit 604. Consistent with the present disclosure, the characteristic ofplanogram compliance may include at least one of: product facing,product placement, planogram compatibility, price correlation, promotionexecution, product homogeneity, restocking rate, and planogramcompliance of adjacent products.

FIG. 7B provides a flowchart representing an exemplary method 720 foracquiring images of products in retail store 105, in accordance withexample embodiments of the present disclosure. For purposes ofillustration, in the following description, reference is made to certaincomponents of system 500 as deployed in the configuration depicted inFIG. 6A. It will be appreciated, however, that other implementations arepossible and that other configurations may be utilized to implement theexemplary method. It will also be readily appreciated that theillustrated method may be altered to modify the order of steps, deletesteps, or further include additional steps.

At step 722, at least one processor contained in a second housing mayreceive from at least one image capture device contained in at least onefirst housing fixedly mounted on a retail shelving unit a plurality ofimages of an opposing retail shelving unit. For example, at least oneprocessor contained in second housing 504A may receive from at least oneimage capture device 506 contained in first housing 502A (fixedlymounted on first retail shelving unit 602) a plurality of images ofsecond retail shelving unit 604. The plurality of images may be capturedand collected during a period of time (e.g., a minute, an hour, sixhours, a day, a week, or more).

At step 724, the at least one processor contained in the second housingmay analyze the plurality of images acquired by the at least one imagecapture device. In one embodiment, at least one processor contained insecond housing 504A may use any suitable image analysis technique (forexample, object recognition, object detection, image segmentation,feature extraction, optical character recognition (OCR), object-basedimage analysis, shape region techniques, edge detection techniques,pixel-based detection, artificial neural networks, convolutional neuralnetworks, etc.) to identify objects in the plurality of images. In oneexample, the at least one processor contained in second housing 504A maydetermine the number of products located in second retail shelving unit604. In another example, the at least one processor contained in secondhousing 504A may detect one or more objects in an area between firstretail shelving unit 602 and second retail shelving unit 604.

At step 726, the at least one processor contained in the second housingmay identify in the plurality of images a first image that includes arepresentation of at least a portion of an object located in an areabetween the retail shelving unit and the opposing retail shelving unit.In step 728, the at least one processor contained in the second housingmay identify in the plurality of images a second image that does notinclude any object located in an area between the retail shelving unitand the opposing retail shelving unit. In one example, the object in thefirst image may be an individual, such as a customer or a storeassociate. In another example, the object in the first image may be aninanimate object, such as carts, boxes, products, etc.

At step 730, the at least one processor contained in the second housingmay instruct a network interface contained in the second housing,fixedly mounted on the retail shelving unit separate from the at leastone first housing, to transmit the second image to a remote server andto avoid transmission of the first image to the remote server. Inaddition, the at least one processor may issue a notification when anobject blocks the field of view of the image capturing device for morethan a predefined period of time (e.g., at least 30 minutes, at least 75minutes, at least 150 minutes).

Embodiments of the present disclosure may automatically assesscompliance of one or more store shelves with a planogram. For example,embodiments of the present disclosure may use signals from one or moresensors to determine placement of one or more products on store shelves.The disclosed embodiments may also use one or more sensors to determineempty spaces on the store shelves. The placements and empty spaces maybe automatically assessed against a digitally encoded planogram. Aplanogram refers to any data structure or specification that defines atleast one product characteristic relative to a display structureassociated with a retail environment (such as store shelf or area of oneor more shelves). Such product characteristics may include, among otherthings, quantities of products with respect to areas of the shelves,product configurations or product shapes with respect to areas of theshelves, product arrangements with respect to areas of the shelves,product density with respect to areas of the shelves, productcombinations with respect to areas of the shelves, etc. Althoughdescribed with reference to store shelves, embodiments of the presentdisclosure may also be applied to end caps or other displays; bins,shelves, or other organizers associated with a refrigerator or freezerunits; or any other display structure associated with a retailenvironment.

The embodiments disclosed herein may use any sensors configured todetect one or more parameters associated with products (or a lackthereof). For example, embodiments may use one or more of pressuresensors, weight sensors, light sensors, resistive sensors, capacitivesensors, inductive sensors, vacuum pressure sensors, high pressuresensors, conductive pressure sensors, infrared sensors, photo-resistorsensors, photo-transistor sensors, photo-diodes sensors, ultrasonicsensors, or the like. Some embodiments may use a plurality of differentkinds of sensors, for example, associated with the same or overlappingareas of the shelves and/or associated with different areas of theshelves. Some embodiments may use a plurality of sensors configured tobe placed adjacent a store shelf, configured for location on the storeshelf, configured to be attached to, or configured to be integrated withthe store shelf. In some cases, at least part of the plurality ofsensors may be configured to be placed next to a surface of a storeshelf configured to hold products. For example, the at least part of theplurality of sensors may be configured to be placed relative to a partof a store shelf such that the at least part of the plurality of sensorsmay be positioned between the part of a store shelf and products placedon the part of the shelf In another embodiment, the at least part of theplurality of sensors may be configured to be placed above and/or withinand/or under the part of the shelf.

In one example, the plurality of sensors may include light detectorsconfigured to be located such that a product placed on the part of theshelf may block at least some of the ambient light from reaching thelight detectors. The data received from the light detectors may beanalyzed to detect a product or to identify a product based on the shapeof a product placed on the part of the shelf. In one example, the systemmay identify the product placed above the light detectors based on datareceived from the light detectors that may be indicative of at leastpart of the ambient light being blocked from reaching the lightdetectors. Further, the data received from the light detectors may beanalyzed to detect vacant spaces on the store shelf. For example, thesystem may detect vacant spaces on the store shelf based on the receiveddata that may be indicative of no product being placed on a part of theshelf. In another example, the plurality of sensors may include pressuresensors configured to be located such that a product placed on the partof the shelf may apply detectable pressure on the pressure sensors.Further, the data received from the pressure sensors may be analyzed todetect a product or to identify a product based on the shape of aproduct placed on the part of the shelf. In one example, the system mayidentify the product placed above the pressure sensors based on datareceived from the pressure sensors being indicative of pressure beingapplied on the pressure sensors. In addition, the data from the pressuresensors may be analyzed to detect vacant spaces on the store shelf, forexample based on the readings being indicative of no product beingplaced on a part of the shelf, for example, when the pressure readingsare below a selected threshold. Consistent with the present disclosure,inputs from different types of sensors (such as pressure sensors, lightdetectors, etc.) may be combined and analyzed together, for example todetect products placed on a store shelf, to identify shapes of productsplaced on a store shelf, to identify types of products placed on a storeshelf, to identify vacant spaces on a store shelf, and so forth.

With reference to FIG. 8A and consistent with the present disclosure, astore shelf 800 may include a plurality of detection elements, e.g.,detection elements 801A and 801B. In the example of FIG. 8A, detectionelements 801A and 801B may comprise pressure sensors and/or other typeof sensors for measuring one or more parameters (such as resistance,capacitance, or the like) based on physical contact (or lack thereof)with products, e.g., product 803A and product 803B. Additionally oralternatively, detection elements configured to measure one or moreparameters (such as current induction, magnetic induction, visual orother electromagnetic reflectance, visual or other electromagneticemittance, or the like) may be included to detect products based onphysical proximity (or lack thereof) to products. Consistent with thepresent disclosure, the plurality of detection elements may beconfigured for location on shelf 800. The plurality of detectionelements may be configured to detect placement of products when theproducts are placed above at least part of the plurality of detectionelements. Some embodiments of the disclosure, however, may be performedwhen at least some of the detection elements may be located next toshelf 800 (e.g., for magnetometers or the like), across from shelf 800(e.g., for image sensors or other light sensors, light detection andranging (LIDAR) sensors, radio detection and ranging (RADAR) sensors, orthe like), above shelf 800 (e.g., for acoustic sensors or the like),below shelf 800 (e.g., for pressure sensors or the like), or any otherappropriate spatial arrangement. Although depicted as standalone unitsin the example of FIG. 8A, the plurality of detection elements may formpart of a fabric (e.g., a smart fabric or the like), and the fabric maybe positioned on a shelf to take measurements. For example, two or moredetection elements may be integrated together into a single structure(e.g., disposed within a common housing, integrated together within afabric or mat, etc.). In some examples, detection elements (such asdetection elements 801A and 801B) may be placed adjacent to (or placedon) store shelves as described above. Some examples of detectionelements may include pressure sensors and/or light detectors configuredto be placed above and/or within and/or under a store shelf as describedabove.

Detection elements associated with shelf 800 may be associated withdifferent areas of shelf 800. For example, detection elements 801A and801B are associated with area 805A while other detection elements areassociated with area 805B. Although depicted as rows, areas 805A and805B may comprise any areas of shelf 800, whether contiguous (e.g., asquare, a rectangular, or other regular or irregular shape) or not(e.g., a plurality of rectangles or other regular and/or irregularshapes). Such areas may also include horizontal regions between shelves(as shown in FIG. 8A) or may include vertical regions that include arcaof multiple different shelves (e.g., columnar regions spanning overseveral different horizontally arranged shelves). In some examples, theareas may be part of a single plane. In some examples, each area may bepart of a different plane. In some examples, a single area may be partof a single plane or be divided across multiple planes.

One or more processors (e.g., processing device 202) configured tocommunicate with the detection elements (e.g., detection elements 801Aand 801B) may detect first signals associated with a first arca (e.g.,areas 805A and/or 805B) and second signals associated with a secondarca. In some embodiments, the first area may, in part, overlap with thesecond area. For example, one or more detection elements may beassociated with the first area as well as the second area and/or one ormore detection elements of a first type may be associated with the firstarea while one or more detection elements of a second type may beassociated with the second area overlapping, at least in part, the firstarea. In other embodiments, the first area and the second area may bespatially separate from each other.

The one or more processors may, using the first and second signals,determine that one or more products have been placed in the first areawhile the second area includes at least one empty area. For example, ifthe detection elements include pressure sensors, the first signals mayinclude weight signals that match profiles of particular products (suchas the mugs or plates depicted in the example of FIG. 8A), and thesecond signals may include weight signals indicative of the absence ofproducts (e.g., by being equal to or within a threshold of a defaultvalue such as atmospheric pressure or the like). The disclosed weightsignals may be representative of actual weight values associated with aparticular product type or, alternatively, may be associated with arelative weight value sufficient to identify the product and/or toidentify the presence of a product. In some cases, the weight signal maybe suitable for verifying the presence of a product regardless ofwhether the signal is also sufficient for product identification. Inanother example, if the detection elements include light detectors (asdescribed above), the first signals may include light signals that matchprofiles of particular products (such as the mugs or plates depicted inthe example of FIG. 8A), and the second signals may include lightsignals indicative of the absence of products (e.g., by being equal toor within a threshold of a default value such as values corresponding toambient light or the like). For example, the first light signals may beindicative of ambient light being blocked by particular products, whilethe second light signals may be indicative of no product blocking theambient light. The disclosed light signals may be representative ofactual light patterns associated with a particular product type or,alternatively, may be associated with light patterns sufficient toidentify the product and/or to identify the presence of a product.

The one or more processors may similarly process signals from othertypes of sensors. For example, if the detection elements includeresistive or inductive sensors, the first signals may includeresistances, voltages, and/or currents that match profiles of particularproducts (such as the mugs or plates depicted in the example of FIG. 8Aor elements associated with the products, such as tags, etc.), and thesecond signals may include resistances, voltages, and/or currentsindicative of the absence of products (e.g., by being equal to or withina threshold of a default value such as atmospheric resistance, a defaultvoltage, a default current, corresponding to ambient light, or thelike). In another example, if the detection elements include acoustics,LIDAR, RADAR, or other reflective sensors, the first signals may includepatterns of returning waves (whether sound, visible light, infraredlight, radio, or the like) that match profiles of particular products(such as the mugs or plates depicted in the example of FIG. 8A), and thesecond signals may include patterns of returning waves (whether sound,visible light, infrared light, radio, or the like) indicative of theabsence of products (e.g., by being equal to or within a threshold of apattern associated with an empty shelf or the like).

Any of the profile matching described above may include direct matchingof a subject to a threshold. For example, direct matching may includetesting one or more measured values against the profile value(s) withina margin of error; mapping a received pattern onto a profile patternwith a residual having a maximum, minimum, integral, or the like withinthe margin of error; performing an autocorrelation, Fourier transform,convolution, or other operation on received measurements or a receivedpattern and comparing the resultant values or function against theprofile within a margin of error; or the like. Additionally oralternatively, profile matching may include fuzzy matching betweenmeasured values and/or patterns and a database of profiles such that aprofile with a highest level of confidence according to the fuzzysearch. Moreover, as depicted in the example of FIG. 8A, products, suchas product 803B, may be stacked and thus associated with a differentprofile when stacked than when standalone.

Any of the profile matching described above may include use of one ormore machine learning techniques. For example, one or more artificialneural networks, random forest models, or other models trained onmeasurements annotated with product identifiers may process themeasurements from the detection elements and identify productstherefrom. In such embodiments, the one or more models may useadditional or alternative input, such as images of the shelf (e.g., fromcapturing devices 125 of FIGS. 4A-4C explained above) or the like.

Based on detected products and/or empty spaces, determined using thefirst signals and second signals, the one or more processors maydetermine one or more aspects of planogram compliance. For example, theone or more processors may identify products and their locations on theshelves, determine quantities of products within particular areas (e.g.,identifying stacked or clustered products), identify facing directionsassociated with the products (e.g., whether a product is outward facing,inward facing, askew, or the like), or the like. Identification of theproducts may include identifying a product type (e.g., a bottle of soda,a loaf of broad, a notepad, or the like) and/or a product brand (e.g., aCoca-Cola® bottle instead of a Sprite® bottle, a Starbucks® coffeetumbler instead of a Tervis® coffee tumbler, or the like). Productfacing direction and/or orientation, for example, may be determinedbased on a detected orientation of an asymmetric shape of a product baseusing pressure sensitive pads, detected density of products, etc. Forexample, the product facing may be determined based on locations ofdetected product bases relative to certain areas of a shelf (e.g., alonga front edge of a shelf), etc. Product facing may also be determinedusing image sensors, light sensors, or any other sensor suitable fordetecting product orientation.

The one or more processors may generate one or more indicators of theone or more aspects of planogram compliance. For example, an indicatormay comprise a data packet, a data file, or any other data structureindicating any variations from a planogram, e.g., with respect toproduct placement such as encoding intended coordinates of a product andactual coordinates on the shelf, with respect to product facingdirection and/or orientation such as encoding indicators of locationsthat have products not facing a correct direction and/or in an undesiredorientation, or the like.

In addition to or as an alternative to determining planogram compliance,the one or more processors may detect a change in measurements from oneor more detection elements. Such measurement changes may trigger aresponse. For example, a change of a first type may trigger capture ofat least one image of the shelf (e.g., using capturing devices 125 ofFIGS. 4A-4C explained above) while a detected change of a second typemay cause the at least one processor to forgo such capture. A first typeof change may, for example, indicate the moving of a product from onelocation on the shelf to another location such that planogram compliancemay be implicated. In such cases, it may be desired to capture an imageof the product rearrangement in order to assess or reassess productplanogram compliance. In another example, a first type of change mayindicate the removal of a product from the shelf, e.g., by employeestore associate due to damage, by a customer to purchase, or the like.On the other hand, a second type of change may, for example, indicatethe removal and replacement of a product to the same (within a margin oferror) location on the shelf, e.g., by a customer to inspect the item.In cases where products are removed from a shelf, but then replaced onthe shelf (e.g., within a particular time window), the system may forgoa new image capture, especially if the replaced product is detected in alocation similar to or the same as its recent, original position.

With reference to FIG. 8B and consistent with the present disclosure, astore shelf 850 may include a plurality of detection elements, e.g.,detection elements 851A and 851B. In the example of FIG. 8B, detectionelements 851A and 851B may comprise light sensors and/or other sensorsmeasuring one or more parameters (such as visual or otherelectromagnetic reflectance, visual or other electromagnetic emittance,or the like) based on electromagnetic waves from products, e.g., product853A and product 853B. Additionally or alternatively, as explained abovewith respect to FIG. 8B, detection elements 851A and 851B may comprisepressure sensors, other sensors measuring one or more parameters (suchas resistance, capacitance, or the like) based on physical contact (orlack thereof) with the products, and/or other sensors that measure oneor more parameters (such as current induction, magnetic induction,visual or other electromagnetic reflectance, visual or otherelectromagnetic emittance, or the like) based on physical proximity (orlack thereof) to products.

Moreover, although depicted as located on shelf 850, some detectionelements may be located next to shelf 850 (e.g., for magnetometers orthe like), across from shelf 850 (e.g., for image sensors or other lightsensors, light detection and ranging (LIDAR) sensors, radio detectionand ranging (RADAR) sensors, or the like), above shelf 850 (e.g., foracoustic sensors or the like), below shelf 850 (e.g., for pressuresensors, light detectors, or the like), or any other appropriate spatialarrangement. Further, although depicted as standalone in the example ofFIG. 8B, the plurality of detection elements may form part of a fabric(e.g., a smart fabric or the like), and the fabric may be positioned ona shelf to take measurements.

Detection elements associated with shelf 850 may be associated withdifferent areas of shelf 850, e.g., area 855A, area 855B, or the like.Although depicted as rows, areas 855A and 855B may comprise any areas ofshelf 850, whether contiguous (e.g., a square, a rectangular, or otherregular or irregular shape) or not (e.g., a plurality of rectangles orother regular and/or irregular shapes).

One or more processors (e.g., processing device 202) in communicationwith the detection elements (e.g., detection elements 851A and 851B) maydetect first signals associated with a first area and second signalsassociated with a second area. Any of the processing of the first andsecond signals described above with respect to FIG. 8A may similarly beperformed for the configuration of FIG. 8B.

In both FIGS. 8A and 8B, the detection elements may be integral to theshelf, part of a fabric or other surface configured for positioning onthe shelf, or the like. Power and/or data cables may form part of theshelf, the fabric, the surface, or be otherwise connected to thedetection elements. Additionally or alternatively, as depicted in FIGS.8A and 8B, individual sensors may be positioned on the shelf. Forexample, the power and/or data cables may be positioned under the shelfand connected through the shelf to the detection elements. In anotherexample, power and/or data may be transmitted wirelessly to thedetection elements (e.g., to wireless network interface controllersforming part of the detection elements). In yet another example, thedetection elements may include internal power sources (such as batteriesor fuel cells).

With reference to FIG. 9 and consistent with the present disclosure, thedetection elements described above with reference to FIGS. 8A and 8B maybe arranged on rows of the shelf in any appropriate configuration. Allof the arrangements of FIG. 9 are shown as a top-down view of a row(e.g., area 805A, area 805B, area 855A, area 855B, or the like) on theshelf. For example, arrangements 910 and 940 are both uniformdistributions of detection elements within a row. However, arrangement910 is also uniform throughout the depth of the row while arrangement940 is staggered. Both arrangements may provide signals that representproducts on the shelf in accordance with spatially uniform measurementlocations. As further shown in FIG. 9 , arrangements 920, 930, 950, and960 cluster detection elements near the front (e.g., a facing portion)of the row. Arrangement 920 includes detection elements at a frontportion while arrangement 930 includes defection elements in a largerportion of the front of the shelf. Such arrangements may save power andprocessing cycles by having fewer detection elements on a back portionof the shelf. Arrangements 950 and 960 include some detection elementsin a back portion of the shelf but these elements are arranged lessdense than detection elements in the front. Such arrangements may allowfor detections in the back of the shelf (e.g., a need to restockproducts, a disruption to products in the back by a customer or a storeassociate, or the like) while still using less power and fewerprocessing cycles than arrangements 910 and 940. And, such arrangementsmay include a higher density of detection elements in regions of theshelf (e.g., a front edge of the shelf) where product turnover rates maybe higher than in other regions (e.g., at areas deeper into a shelf),and/or in regions of the shelf where planogram compliance is especiallyimportant.

FIG. 10A is a flow chart, illustrating an exemplary method 1000 formonitoring planogram compliance on a store shelf, in accordance with thepresently disclosed subject matter. It is contemplated that method 1000may be used with any of the detection element arrays discussed abovewith reference to, for example, FIGS. 8A, 8B and 9 . The order andarrangement of steps in method 1000 is provided for purposes ofillustration. As will be appreciated from this disclosure, modificationsmay be made to process 1000, for example, adding, combining, removing,and/or rearranging one or more steps of process 1000.

Method 1000 may include a step 1005 of receiving first signals from afirst subset of detection elements (e.g., detection elements 801A and801B of FIG. 8A) from among the plurality of detection elements afterone or more of a plurality of products (e.g., products 803A and 803B)are placed on at least one area of the store shelf associated with thefirst subset of detection elements. As explained above with respect toFIGS. 8A and 8B, the plurality of detection elements may be embeddedinto a fabric configured to be positioned on the store shelf.Additionally or alternatively, the plurality of detection elements maybe configured to be integrated with the store shelf. For example, anarray of pressure sensitive elements (or any other type of detector) maybe fabricated as part of the store shelf. In some examples, theplurality of detection elements may be configured to placed adjacent to(or located on) store shelves, as described above.

As described above with respect to arrangements 910 and 940 of FIG. 9 ,the plurality of detection elements may be substantially uniformlydistributed across the store shelf. Alternatively, as described abovewith respect to arrangements 920, 930, 950, and 960 of FIG. 9 , theplurality of detection elements may be distributed relative to the storeshelf such that a first area of the store shelf has a higher density ofdetection elements than a second area of the store shelf. For example,the first area may comprise a front portion of the shelf, and the secondarea may comprise a back portion of the shelf.

In some embodiments, such as those including pressure sensors or othercontact sensors as depicted in the example of FIG. 8A, step 1005 mayinclude receiving the first signals from the first subset of detectionelements as the plurality of products are placed above the first subsetof detection elements. In some embodiments where the plurality ofdetection elements includes pressure detectors, the first signals may beindicative of pressure levels detected by pressure detectorscorresponding to the first subset of detection elements after one ormore of the plurality of products are placed on the at least one area ofthe store shelf associated with the first subset of detection elements.For example, the first signals may be indicative of pressure levelsdetected by pressure detectors corresponding to the first subset ofdetection elements after stocking at least one additional product abovea product previously positioned on the shelf, removal of a product fromthe shelf, or the like. In other embodiments where the plurality ofdetection elements includes light detectors, the first signals may beindicative of light measurements made with respect to one or more of theplurality of products placed on the at least one area of the store shelfassociated with the first subset of detection elements. Specifically,the first signals may be indicative of at least part of the ambientlight being blocked from reaching the light detectors by the one or moreof the plurality of products.

In embodiments including proximity sensors as depicted in the example ofFIG. 8B, step 1005 may include receiving the first signals from thefirst subset of detection elements as the plurality of products areplaced below the first subset of detection elements. In embodimentswhere the plurality of detection elements include proximity detectors,the first signals may be indicative of proximity measurements made withrespect to one or more of the plurality of products placed on the atleast one area of the store shelf associated with the first subset ofdetection elements.

Method 1000 may include step 1010 of using the first signals to identifyat least one pattern associated with a product type of the plurality ofproducts. For example, any of the pattern matching techniques describedabove with respect to FIGS. 8A and 8B may be used for identification. Apattern associated with a product type may include a pattern (e.g., acontinuous ring, a discontinuous ring of a certain number of points, acertain shape, etc.) associated with a base of a single product. Thepattern associated with a product type may also be formed by a group ofproducts. For example, a six pack of soda cans may be associated with apattern including a 2×3 array of continuous rings associated with thesix cans of that product type. Additionally, a grouping of two literbottles may form a detectable pattern including an array (whetheruniform, irregular, or random) of discontinuous rings of pressurepoints, where the rings have a diameter associated with a particular2-liter product. Various other types of patterns may also be detected(e.g., patterns associated with different product types arrangedadjacent to one another, patterns associated with solid shapes (such asa rectangle of a boxed product), etc.). In another example, anartificial neural network configured to recognize product types may beused to analyze the signals received by step 1005 (such as signals frompressure sensors, from light detectors, from contact sensors, and soforth) to determine product types associated with products placed on anarea of a shelf (such as an area of a shelf associated with the firstsubset of detection elements). In yet another example, a machinelearning algorithm trained using training examples to recognize producttypes may be used to analyze the signals received by step 1005 (such assignals from pressure sensors, from light detectors, from contactsensors, and so forth) to determine product types associated withproducts placed on an area of a shelf (such as an area of a shelfassociated with the first subset of detection elements).

In some embodiments, step 1010 may further include accessing a memorystoring data (e.g., memory device 226 of FIG. 2 and/or memory device 314of FIG. 3A) associated with patterns of different types of products. Insuch embodiments, step 1010 may include using the first signals toidentify at least one product of a first type using a first pattern (ora first product model) and at least one product of a second type using asecond pattern (or a second product model). For example, the first typemay include one brand (such as Coca-Cola® or Folgers®) while the secondtype may include another brand (such as Pepsi® or Maxwell House k). Inthis example, a size, shape, point spacing, weight, resistance or otherproperty of the first brand may be different from that of the secondbrand such that the detection elements may differentiate the brands.Such characteristics may also be used to differentiate like-branded, butdifferent products from one another (e.g., a 12-ounce can of Coca Cola,versus a 16 oz bottle of Coca Cola, versus a 2-liter bottle of CocaCola). For example, a soda may have a base detectable by a pressuresensitive pad as a continuous ring. Further, the can of soda may beassociated with a first weight signal having a value recognizable asassociated with such a product. A 16 ounce bottle of soda may beassociated with a base having four or five pressure points, which apressure sensitive pad may detect as arranged in a pattern associatedwith a diameter typical of such a product. The 16 ounce bottle of sodamay also be associated with a second weight signal having a value higherthan the weight signal associated with the 12 ounce can of soda. Furtherstill, a 2 liter bottle of soda may be associated with a base having aring, four or five pressure points, etc. that a pressure sensitive padmay detect as arranged in a pattern associated with a diameter typicalof such a product. The 2 liter bottle of soda may be associated with aweight signal having a value higher than the weight signal associatedwith the 12 ounce can of soda and 16 ounce bottle of soda.

In the example of FIG. 8B, the different bottoms of product 853A andproduct 853B may be used to differentiate the products from each other.For example, detection elements such as pressure sensitive pads may beused to detect a product base shape and size (e.g., ring, pattern ofpoints, asymmetric shape, base dimensions, etc.). Such a base shape andsize may be used (optionally, together with one or more weight signals)to identify a particular product. The signals may also be used toidentify and/or distinguish product types from one another. For example,a first type may include one category of product (such as soda cans)while a second type may include a different category of product (such asnotepads). In another example, detection elements such as lightdetectors may be used to detect a product based on a pattern of lightreadings indicative of a product blocking at least part of the ambientlight from reaching the light detectors. Such pattern of light readingsmay be used to identify product type and/or product category and/orproduct shape. For example, products of a first type may block a firstsubset of light frequencies of the ambient light from reaching the lightdetectors, while products of a second type may block a second subset oflight frequencies of the ambient light from reaching the light detectors(the first subset and second subset may differ). In this case the typeof the products may be determined based on the light frequenciesreaching the light detectors. In another example, products of a firsttype may have a first shape of shades and therefore may block ambientlight from reaching light detectors arranged in one shape, whileproducts of a second type may have a second shape of shades andtherefore may block ambient light from reaching light detectors arrangedin another shape. In this case the type of the products may bedetermined based on the shape of blocked ambient light. Any of thepattern matching techniques described above may be used for theidentification.

Additionally or alternatively, step 1010 may include using the at leastone pattern to determine a number of products placed on the at least onearea of the store shelf associated with the first subset of detectionelements. For example, any of the pattern matching techniques describedabove may be used to identify the presence of one or more product typesand then to determine the number of products of each product type (e.g.,by detecting a number of similarly sized and shaped product bases andoptionally by detecting weight signals associated with each detectedbase). In another example, an artificial neural network configured todetermine the number of products of selected product types may be usedto analyze the signals received by step 1005 (such as signals frompressure sensors, from light detectors, from contact sensors, and soforth) to determine the number of products of selected product typesplaced on an area of a shelf (such as an area of a shelf associated withthe first subset of detection elements). In yet another example, amachine learning algorithm trained using training examples to determinethe number of products of selected product types may be used to analyzethe signals received by step 1005 (such as signals from pressuresensors, from light detectors, from contact sensors, and so forth) todetermine the number of products of selected product types placed on anarca of a shelf (such as an area of a shelf associated with the firstsubset of detection elements). Additionally or alternatively, step 1010may include extrapolating from a stored pattern associated with a singleproduct (or type of product) to determine the number of productsmatching the first signals. In such embodiments, step 1010 may furtherinclude determining, for example based on product dimension data storedin a memory, a number of additional products that may be placed on theat least one area of the store shelf associated with the second subsetof detection elements. For example, step 1010 may include extrapolatingbased on stored dimensions of each product and stored dimensions of theshelf area to determine an area and/or volume available for additionalproducts. Step 1010 may further include extrapolation of the number ofadditional products based on the stored dimensions of each product anddetermined available area and/or volume.

Method 1000 may include step 1015 of receiving second signals from asecond subset of detection elements (e.g., detection elements 851A and851B of FIG. 8B) from among the plurality of detection elements, thesecond signals being indicative of no products being placed on at leastone area of the store shelf associated with the second subset ofdetection elements. Using this information, method 1000 may include step1020 of using the second signals to determine at least one empty spaceon the store shelf. For example, any of the pattern matching techniquesdescribed above may be used to determine that the second signals includedefault values or other values indicative of a lack of product incertain areas associated with a retail store shelf. A default value maybe include, for example, a pressure signal associated with an un-loadedpressure sensor or pressure sensitive mat, indicating that no product islocated in a certain region of a shelf. In another example, a defaultvalue may include signals from light detectors corresponding to ambientlight, indicating that no product is located in a certain region of ashelf.

Method 1000 may include step 1025 of determining, based on the at leastone pattern associated with a detected product and the at least oneempty space, at least one aspect of planogram compliance. As explainedabove with respect to FIGS. 8A and 8B, the aspect of planogramcompliance may include the presence or absence of particular products(or brands), locations of products on the shelves, quantities ofproducts within particular areas (e.g., identifying stacked or clusteredproducts), facing directions associated with the products (e.g., whethera product is outward facing, inward facing, askew, or the like), or thelike. A planogram compliance determination may be made, for example, bydetermining a number of empty spaces on a shelf and determining alocation of the empty spaces on a shelf. The planogram determination mayalso include determining weight signal magnitudes associated withdetected products at the various detected non-empty locations. Thisinformation may be used by the one or more processors in determiningwhether a product facing specification has been satisfied (e.g., whethera front edge of a shelf has a suitable number of products or suitabledensity of products), whether a specified stacking density has beenachieved (e.g., by determining a pattern of detected products and weightsignals of the detected products to determine how many products arestacked at each location), whether a product density specification hasbeen achieved (e.g., by determining a ratio of empty locations toproduct-present locations), whether products of a selected product typeare located in a selected area of the shelf, whether all productslocated in a selected arca of the shelf are of a selected product type,whether a selected number of products (or a selected number of productsof a selected product type) are located in a selected area of the shelf,whether products located in a selected area of a shelf are positioned ina selected orientation, or whether any other aspect of one or moreplanograms has been achieved.

For example, the at least one aspect may include product homogeneity,and step 1025 may further include counting occurrences where a productof the second type is placed on an area of the store shelf associatedwith the first type of product. For example, by accessing a memoryincluding base patterns (or any other type of pattern associated withproduct types, such as product models), the at least one processor maydetect different products and product types. A product of a first typemay be recognized based on a first pattern, and product of a second typemay be recognized based on a second, different pattern (optionally alsobased on weight signal information to aid in differentiating betweenproducts). Such information may be used, for example, to monitor whethera certain region of a shelf includes an appropriate or intended productor product type. Such information may also be useful in determiningwhether products or product types have been mixed (e.g., producthomogeneity). Regarding planogram compliance, detection of differentproducts and their relative locations on a shelf may aid in determiningwhether a product homogeneity value, ratio, etc. has been achieved. Forexample, the at least one processor may count occurrences where aproduct of a second type is placed on an arca of the store shelfassociated with a product of a first type.

Additionally or alternatively, the at least one aspect of planogramcompliance may include a restocking rate, and step 1025 may furtherinclude determining the restocking rate based on a sensed rate at whichproducts are added to the at least one area of the store shelfassociated with the second subset of detection elements. Restocking ratemay be determined, for example, by monitoring a rate at which detectionelement signals change as products are added to a shelf (e.g., whenareas of a pressure sensitive pad change from a default value to aproduct-present value).

Additionally or alternatively, the at least one aspect of planogramcompliance may include product facing, and step 1025 may further includedetermining the product facing based on a number of products determinedto be placed on a selected area of the store shelf at a front of thestore shelf. Such product facing may be determined by determining anumber of products along a certain length of a front edge of a storeshelf and determining whether the number of products complies with, forexample, a specified density of products, a specified number ofproducts, and so forth.

Step 1025 may further include transmitting an indicator of the at leastone aspect of planogram compliance to a remote server. For example, asexplained above with respect to FIGS. 8A and 8B, the indicator maycomprise a data packet, a data file, or any other data structureindicating any variations from a planogram, e.g., with respect toproduct (or brand) placement, product facing direction, or the like. Theremote server may include one or more computers associated with a retailstore (e.g., so planogram compliance may be determined on a local basiswithin a particular store), one or more computers associated with aretail store evaluation body (e.g., so planogram compliance may bedetermined across a plurality of retail stores), one or more computersassociated with a product manufacturer, one or more computers associatedwith a supplier (such as supplier 115), one or more computers associatedwith a market research entity (such as market research entity 110), etc.

Method 1000 may further include additional steps. For example, method1000 may include identifying a change in at least one characteristicassociated with one or more of the first signals (e.g., signals from afirst group or type of detection elements), and in response to theidentified change, triggering an acquisition of at least one image ofthe store shelf. The acquisition may be implemented by activating one ormore of capturing devices 125 of FIGS. 4A-4C, as explained above. Forexample, the change in at least one characteristic associated with oneor more of the first signals may be indicative of removal of at leastone product from a location associated with the at least one area of thestore shelf associated with the first subset of detection elements.Accordingly, method 1000 may include triggering the acquisition todetermine whether restocking, reorganizing, or other intervention isrequired, e.g., to improve planogram compliance. Thus, method 1000 mayinclude identifying a change in at least one characteristic associatedwith one or more of the first signals; and in response to the identifiedchange, trigger a product-related task for a store associate of theretail store.

Additionally or alternatively, method 1000 may be combined with method1050 of FIG. 10B, described below, such that step 1055 is performed anytime after step 1005.

FIG. 10B is a flow chart, illustrating an exemplary method 1050 fortriggering image capture of a store shelf, in accordance with thepresently disclosed subject matter. It is contemplated that method 1050may be used in conjunction with any of the detection element arraysdiscussed above with reference to, for example, FIGS. 8A, 8B and 9 . Theorder and arrangement of steps in method 1050 is provided for purposesof illustration. As will be appreciated from this disclosure,modifications may be made to process 1050, for example, adding,combining, removing, and/or rearranging one or more steps of process1050.

Method 1050 may include a step 1055 of determining a change in at leastone characteristic associated with one or more first signals. Forexample, the first signals may have been captured as part of method 1000of FIG. 10A, described above. For example, the first signals may includepressure readings when the plurality of detection elements includespressure sensors, contact information when the plurality of detectionelements includes contact sensors, light readings when the plurality ofdetection elements includes light detectors (for example, from lightdetectors configured to be placed adjacent to (or located on) a surfaceof a store shelf configured to hold products, as described above), andso forth.

Method 1050 may include step 1060 of using the first signals to identifyat least one pattern associated with a product type of the plurality ofproducts. For example, any of the pattern matching techniques describedabove with respect to FIGS. 8A, 8B, and step 1010 may be used foridentification.

Method 1050 may include step 1065 of determining a type of eventassociated with the change. For example, a type of event may include aproduct removal, a product placement, movement of a product, or thelike.

Method 1050 may include step 1070 of triggering an acquisition of atleast one image of the store shelf when the change is associated with afirst event type. For example, a first event type may include removal ofa product, moving of a product, or the like, such that the first eventtype may trigger a product-related task for a store associate of theretail store depending on analysis of the at least one image. Theacquisition may be implemented by activating one or more of capturingdevices 125 of FIGS. 4A-4C, as explained above. In some examples, thetriggered acquisition may include an activation of at least oneprojector (such as projector 632). In some examples, the triggeredacquisition may include acquisition of color images, depth images,stereo images, active stereo images, time of flight images, LIDARimages, RADAR images, and so forth.

Method 1050 may include a step (not shown) of forgoing the acquisitionof at least one image of the store shelf when the change is associatedwith a second event type. For example, a second event type may includereplacement of a removed product by a customer, stocking of a shelf by astore associate, or the like. As another example, a second event typemay include removal, placement, or movement of a product that isdetected within a margin of error of the detection elements and/ordetected within a threshold (e.g., removal of only one or two products;movement of a product by less than 5 cm, 20 cm, or the like; moving of afacing direction by less than 10 degrees; or the like), such that noimage acquisition is required.

FIGS. 11A-11E illustrate example outputs based on data automaticallyderived from machine processing and analysis of images captured inretail store 105 according to disclosed embodiments. FIG. 11Aillustrates an optional output for market research entity 110. FIG. 11Billustrates an optional output for supplier 115. FIGS. 11C and 11Dillustrate optional outputs for store associates of retail store 105.And FIG. 11E illustrates optional outputs for user 120.

FIG. 11A illustrates an example graphical user interface (GUI) 500 foroutput device 145A, representative of a GUI that may be used by marketresearch entity 110. Consistent with the present disclosure, marketresearch entity 110 may assist supplier 115 and other stakeholders inidentifying emerging trends, launching new products, and/or developingmerchandising and distribution plans across a large number of retailstores 105. By doing so, market research entity 110 may assist supplier115 in growing product presence and maximizing or increasing new productsales. As mentioned above, market research entity 110 may be separatedfrom or part of supplier 115. To successfully launch a new product,supplier 115 may use information about what really happens in retailstore 105. For example, supplier 115 may want to monitor how marketingplans are being executed and to learn what other competitors are doingrelative to certain products or product types. Embodiments of thepresent disclosure may allow market research entity 110 and suppliers115 to continuously monitor product-related activities at retail stores105 (e.g., using system 100 to generate various metrics or informationbased on automated analysis of actual, timely images acquired from theretail stores). For example, in some embodiments, market research entity110 may track how quickly or at what rate new products are introduced toretail store shelves, identify new products introduced by variousentities, assess a supplier's brand presence across different retailstores 105, among many other potential metrics.

In some embodiments, server 135 may provide market research entity 110with information including shelf organization, analysis of skewproductivity trends, and various reports aggregating information onproducts appearing across large numbers of retail stores 105. Forexample, as shown in FIG. 11A, GUI 1100 may include a first display area1102 for showing a percentage of promotion campaign compliance indifferent retail stores 105. GUI 1100 may also include a second displayarea 1104 showing a graph illustrating sales of a certain productrelative to the percentage of out of shelf. GUI 1100 may also include athird display area 1106 showing actual measurements of different factorsrelative to target goals (e.g., planogram compliance, restocking rate,price compliance, and other metrics). The provided information mayenable market research entity 110 to give supplier 115 informed shelvingrecommendations and fine-tune promotional strategies according toin-store marketing trends, to provide store managers with a comparisonof store performances in comparison to a group of retail stores 105 orindustry wide performances, and so forth.

FIG. 11B illustrates an example GUI 1110 for output device 145B used bysupplier 115. Consistent with the present disclosure, server 135 may usedata derived from images captured in a plurality of retail stores 105 torecommend a planogram, which often determines sales success of differentproducts. Using various analytics and planogram productivity measures,server 135 may help supplier 115 to determine an effective planogramwith assurances that most if not all retail stores 105 may execute theplan. For example, the determined planogram may increase the probabilitythat inventory is available for each retail store 105 and may bedesigned to decrease costs or to keep costs within a budget (such asinventory costs, restocking costs, shelf space costs, etc.). Server 135may also provide pricing recommendations based on the goals of supplier115 and other factors. In other words, server 135 may help supplier 115understand how much room to reserve for different products and how tomake them available for favorable sales and profit impact (for example,by choosing the size of the shelf dedicated to a selected product, thelocation of the shelf, the height of the shelf, the neighboringproducts, and so forth). In addition, server 135 may monitor nearreal-time data from retail stores 105 to determine or confirm thatretail stores 105 are compliant with the determined planogram ofsupplier 115. As used herein, the term “near real-time data,” in thecontext of this disclosure, refers to data acquired or generated, etc.,based on sensor readings and other inputs (such as data from imagesensors, audio sensors, pressure sensors, checkout stations, etc.) fromretail store 105 received by system 100 within a predefined period oftime (such as time periods having durations of less than a second, lessthan a minute, less than an hour, less than a day, less than a week,etc.).

In some embodiments, server 135 may generate reports that summarizeperformance of the current assortment and the planogram compliance.These reports may advise supplier 115 of the category and the itemperformance based on individual SKU, sub segments of the category,vendor, and region. In addition, server 135 may provide suggestions orinformation upon which decisions may be made regarding how or when toremove markdowns and when to replace underperforming products. Forexample, as shown in FIG. 11B, GUI 1110 may include a first display area1112 for showing different scores of supplier 115 relative to scoresassociated with its competitors. GUI 1110 may also include a seconddisplay area 1114 showing the market share of each competitor. GUI 1110may also include a third display area 1116 showing retail measurementsand distribution of brands. GUI 1110 may also include a fourth displayarea 1118 showing a suggested planogram. The provided information mayhelp supplier 115 to select preferred planograms based on projected orobserved profitability, etc., and to ensure that retail stores 105 arefollowing the determined planogram.

FIGS. 11C and 11D illustrate example GUIs for output devices 145C, whichmay be used by store associates of retail store 105. FIG. 11C depicts aGUI 1120 for a manager of retail store 105 designed for a desktopcomputer, and FIG. 11D depicts GUI 1130 and 1140 for store staffdesigned for a handheld device. In-store execution is one of thechallenges retail stores 105 have in creating a positive customerexperience. Typical in-store execution may involve dealing with ongoingservice events, such as a cleaning event, a restocking event, arearrangement event, and more. In some embodiments, system 100 mayimprove in-store execution by providing adequate visibility to ensurethat the right products are located at preferred locations on the shelf.For example, using near real-time data (e.g., captured images of storeshelves) server 135 may generate customized online reports. Storemanagers and regional managers, as well as other stakeholders, mayaccess custom dashboards and online reports to see how in-storeconditions (such as, planogram compliance, promotion compliance, pricecompliance, etc.) are affecting sales. This way, system 100 may enablemanagers of retail stores 105 to stay on top of burning issues acrossthe floor and assign store associates to address issues that maynegatively impact the customer experience.

In some embodiments, server 135 may cause real-time automated alertswhen products are out of shelf (or near out of shelf), when pricing isinaccurate, when intended promotions are absent, and/or when there areissues with planogram compliance, among others. In the example shown inFIG. 11C, GUI 1120 may include a first display area 1122 for showing theaverage scores (for certain metrics) of a specific retail store 105 overa selected period of time. GUI 1120 may also include a second displayarea 1124 for showing a map of the specific retail store 105 withreal-time indications of selected in-store execution events that requireattention, and a third display area 1126 for showing a list of theselected in-store execution events that require attention. In anotherexample, shown in FIG. 11D, GUI 1130 may include a first display area1132 for showing a list of notifications or text messages indicatingselected in-store execution events that require attention. Thenotifications or text messages may include a link to an image (or theimage itself) of the specific aisle with the in-store execution event.In another example, shown in FIG. 11D, GUI 1140 may include a firstdisplay area 1142 for showing a display of a video stream captured byoutput device 145C (e.g., a real-time display or a near real-timedisplay) with augmented markings indicting a status of planogramcompliance for each product (e.g., correct place, misplaced, not inplanogram, empty, and so forth). GUI 1140 may also include a seconddisplay area 1144 for showing a summary of the planogram compliance forall the products identified in the video stream captured by outputdevice 145C. Consistent with the present disclosure, server 135 maygenerate within minutes actionable tasks to improve store execution.These tasks may help store associates of retail store 105 to quicklyaddress situations that may negatively impact revenue and customerexperience in the retail store 105.

FIG. 11E illustrates an example GUI 1150 for output device 145D used byan online customer of retail store 105. Traditional online shoppingsystems present online customers with a list of products. Productsselected for purchase may be placed into a virtual shopping cart untilthe customers complete their virtual shopping trip. Virtual shoppingcarts may be examined at any time, and their contents may be edited ordeleted. However, common problems of traditional online shopping systemsarise when the list of products on the website does not correspond withthe actual products on the shelf. For example, an online customer mayorder a favorite cookie brand without knowing that the cookie brand isout-of-stock. Consistent with some embodiments, system 100 may use imagedata acquired by capturing devices 125 to provide the online customerwith a near real-time display of the retail store and a list of theactual products on the shelf based on near real-time data. In oneembodiment, server 135 may select images without occlusions in the fieldof view (e.g., without other customers, carts, etc.) for the nearreal-time display. In one embodiment, server 135 may blur or erasedepictions of customers and other people from the near real-timedisplay. As used herein, the term “near real-time display,” in thecontext of this disclosure, refers to image data captured in retailstore 105 that was obtained by system 100 within a predefined period oftime (such as less than a second, less than a minute, less than about 30minutes, less than an hour, less than 3 hours, or less than 12 hours)from the time the image data was captured.

Consistent with the present disclosure, the near real-time display ofretail store 105 may be presented to the online customer in a mannerenabling easy virtual navigation in retail store 105. For example, asshown in FIG. 11E, GUI 1150 may include a first display area 1152 forshowing the near real-time display and a second display area 1154 forshowing a product list including products identified in the nearreal-time display. In some embodiments, first display area 1152 mayinclude different GUI features (e.g., tabs 1156) associated withdifferent locations or departments of retail store 105. By selectingeach of the GUI features, the online customer may virtually jump todifferent locations or departments in retail store 105. For example,upon selecting the “bakery” tab, GUI 1150 may present a near real-timedisplay of the bakery of retail store 105. In addition, first displayarea 1152 may include one or more navigational features (e.g., arrows1158A and 1158B) for enabling the online customer to virtually movewithin a selected department and/or virtually walk through retail store105. Server 135 may be configured to update the near real-time displayand the product list upon determining that the online customer wants tovirtually move within retail store 105. For example, after identifying aselection of arrow 1158B, server 135 may present a different section ofthe dairy department and may update the product list accordingly. Inanother example, server 135 may update the near-real time display andthe product list in response to new captured images and new informationreceived from retail store 105. Using GUI 1150, the online customer mayhave the closest shopping experience without actually being in retailstore 105. For example, an online customer can visit the vegetabledepartment and decide not to buy tomatoes after seeing that they are notripe enough.

As discussed above, shopping in retail stores is a prevalent part ofmodern-day life. To improve customer experience, during a shopper'svisit to a retail store, store owners may provide a variety ofconvenient ways for the shoppers to select and purchase products. Forexample, one common way of improving customer experience has been toprovide self-checkout counters in a retail store, allowing shoppers toquickly purchase their desired items and leave the store without needingto wait for a store associate to help with the purchasing process. Thedisclosed embodiments provide another method of improving customerexperience in the form of frictionless checkout.

As used herein, frictionless checkout refers to any checkout process fora retail environment with at least one aspect intended to expedite,simplify, or otherwise improve an experience for customers. In someembodiments, frictionless checkout may reduce or eliminate the need totake inventory of products being purchased by the customer at checkout.For example, this may include tracking the selection of products made bythe shopper so that they are already identified at the time of checkout.The tracking of products may occur through the implementation of sensorsused to track movement of the shopper and/or products within the retailenvironment, as described throughout the present disclosure.Additionally or alternatively, frictionless checkout may include anexpedited or simplified payment procedure. For example, if a retailstore has access to payment information associated with a shopper, thepayment information may be used to receive payment for productspurchased by the shopper automatically or upon selection and/orconfirmation of the payment information by the shopper. In someembodiments, frictionless checkout may involve some interaction betweenthe shopper and a store associate or checkout device or terminal. Inother embodiments, frictionless checkout may not involve any interactionbetween the shopper and a store associate or checkout device orterminal. For example, the shopper may walk out of the store with theselected products and a payment transaction may occur automatically.While the term “frictionless” is used for purposes of simplicity, it isto be understood that this encompasses semi frictionless checkouts aswell. Accordingly, various types of checkout experiences may beconsidered “frictionless,” and the present disclosure is not limited toany particular form or degree of frictionless checkout.

It may be important to determine whether a customer qualifies forfrictionless checkout. For example, a customer who has a good credithistory or history of timely payments for prior purchases may qualifyfor frictionless checkout at a retail store. In contrast, a customerhaving a bad credit history or repeated incidents of delayed or missedpayments for the purchase of goods may not qualify for frictionlesscheckout. In other cases, a store owner may require immediate or instore payment for a high-value product. For example, in an electronicsstore, the store owner may require immediate or in-store payment forproducts such as high-definition televisions, high-end home theatersystems, high-end stereos, etc. A shopper entering the electronics storeand selecting one or more of these high-end items for purchase may notqualify for frictionless checkout. On the other hand, a shopper whopurchases a relatively lower-priced its item, for example, a set of USBflash drives, a wireless mouse, etc., may be eligible for frictionlesscheckout. The disclosed methods and systems may provide a visualindicator that may indicate whether a shopper is eligible forfrictionless checkout.

In some embodiments, a non-transitory computer-readable medium mayinclude instructions that when executed by a processor may cause theprocessor to perform a method for determining whether shoppers areeligible for frictionless checkout. For example, as discussed above, thedisclosed system may include one or more servers 135, which may includeone or more processing devices 202. Processing device 202 may beconfigured to execute one or more instructions stored in anon-transitory computer-readable storage medium. As also discussedabove, the non-transitory computer-readable medium may include one ormore of random access memory (RAM), read-only memory (ROM), volatilememory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives,disks, any other optical data storage medium, any physical medium withpatterns of holes, a RAM, a PROM, an EPROM, a FLASH-EPROM or any otherflash memory, NVRAM, a cache, a register, any other memory chip orcartridge, and networked versions of the same, etc.

In some embodiments, the method may include obtaining image datacaptured using a plurality of image sensors positioned in a retailstore. For example, as discussed above, a retail store (e.g., 105A,105B, 105C, etc., see FIG. 1 ) may include one or more capturing devices125 configured to capture one or more images. Capturing devices 125 mayinclude one or more of a digital camera, a time-of-flight camera, astereo camera, an active stereo camera, a depth camera, a Lidar system,a laser scanner, CCD based devices, etc. Capturing devices 125 may bestationary or movable devices mounted to walls or shelves in the retailstores (e.g., 105A, 105B, 105C, etc.). It is also contemplated thatcapturing devices 125 may be handheld devices (e.g., a smartphone, atablet, a mobile station, a personal digital assistant, a laptop, etc.),a wearable device (e.g., smart glasses, a smartwatch, a clip-on camera,etc.) or may be attached to a robotic device (e.g., drone, robot, etc.).It is further contemplated that capturing devices 125 may be held orworn by a shopper, a store associate, or by one or more other personspresent in retail stores 105.

One or more of capturing devices 125 may include one or more imagesensors 310, which may include one or more semiconductor charge-coupleddevices (CCD), active pixel sensors in complementarymetal-oxide-semiconductor (CMOS), or N-type metal-oxide-semiconductors(NMOS, Live MOS), etc. The one or more image sensors 310 in retailstores 105 may be configured to capture images of one or more persons(e.g., shoppers, store associates, etc.), one or more shelves 350, oneor more items 803A, 803B, 853A, etc. on shelves 350, and/or otherobjects (e.g., shopping carts, checkout counters, walls, columns, poles,aisles, pathways between aisles), etc. The images may be in the form ofimage data, which may include, for example, pixel data streams, digitalimages, digital video streams, data derived from captured images, etc.

In some embodiments, the method may include analyzing the image data toidentify at least one shopper at one or more locations of the retailstore. For example, processing device 202 may analyze the image dataobtained by the one or more image sensors 310 to identify one or morepersons or objects in the image data. As used herein, the term identifymay broadly refer to determining an existence of a person or a productin the image data. It is also contemplated, however, that in someembodiments identifying a person in the image data may includerecognizing a likeness of the person and associating an identifier(e.g., name, customer ID, account number, telephone number, etc.) withthe recognized person. It is contemplated that processing device 202 mayuse any suitable image analysis technique, for example, including one ormore of object recognition, object detection, image segmentation,feature extraction, optical character recognition (OCR), object-basedimage analysis, shape region techniques, edge detection techniques,pixel-based detection, artificial neural networks, convolutional neuralnetworks, etc., to identify one or more persons or objects in the imagedata. It is further contemplated that processing device 202 may accessone or more databases 140 to retrieve one or more reference images oflikenesses of one or more persons. Further, processing device 202 mayuse one or more of the image analysis techniques discussed above tocompare the images retrieved from database 140 with the image datareceived from the one or more image sensors 310 to recognize thelikeness of one or more shoppers in the image data. It is alsocontemplated that processing device 202 may retrieve other identifyinginformation (e.g., name, customers ID, account number, telephone number,etc.) associated with the images retrieved from database 140 based on,for example, profiles of the one or more shoppers stored in database140. In some embodiments, processing device 202 may also be configuredto employ machine learning algorithms or artificial neural networks torecognize and identify one or more shoppers in the image data obtainedby image sensors 310.

In some embodiments, the method may include detecting, based on theanalysis of the image data, at least one product interaction eventassociated with an action of the at least one shopper at the one or morelocations of the retail store. For example, as a shopper passes throughthe retail store, a shopper may interact with one or more productslocated in the store by performing one or more actions. For example, asillustrated in FIG. 12A, shopper 1202 may be standing near shelf 850that may be carrying products 1210, 1212, 1214, etc. Shopper 1202 mayhave shopping cart 1220. In some embodiments, the action of the at leastone shopper may include removing a product from a shelf associated withthe retail store. For example, as illustrated in FIG. 12A, shopper 1202may interact with the one or more products 1210, 1212, 1214, etc., bypicking up product 1210 and removing product 1210 from shelf 850. Insome embodiments, the action of the at least one shopper may includereturning a product to a shelf associated with the retail store. Forexample, shopper may pick up a product (e.g., 1210, 1212, 1214, etc.) byremoving product 1210 from shelf 850 associated with retail store (e.g.,105A, 105B, 105C, etc.), inspect product 1210, position product 1210 invarious orientations, return product 1210 back to shelf 850, placeproduct 1210 in shopping cart 1220, remove product 1210 from shoppingcart 1220, and/or move product 1210 from one location to another (e.g.,move product 1210 from shelf 850 to a different position on the sameshelf, or to another shelf, etc.). Each of these actions by shopper 1202may constitute a product interaction event. Other examples of productinteraction events may include, for example, shopper 1202 picking up aproduct (e.g., 1210, 1212, 1214, etc.) and checking its price using aprice scanner, shopper 1202 picking up a plurality of products (e.g.,one or more of 1210, 1212, 1214, etc.), shopper 1202 returning some ofthe plurality of products (e.g., one or more of 1210, 1212, 1214, etc.)previously removed by shopper 1202 from shelf 850, etc. It is alsocontemplated that a product interaction event may include a combinationof one or more of the actions or events described above.

Processing device 202 may analyze image data received from one or moreimage sensors 310 to detect occurrence of one or more of the productinteraction events discussed above. Processing device 202 may employ oneor more of the image analysis techniques including, for example, objectrecognition, object detection, image segmentation, feature extraction,optical character recognition (OCR), object-based image analysis, shaperegion techniques, edge detection techniques, pixel-based detection,artificial neural networks, convolutional neural networks, etc., todetect the one or more of product interaction events discussed above. Itis also contemplated that processing device 202 may analyze the imagedata obtained by the one or more sensors 310 at a single location or ata plurality of locations in retail stores 125.

In some embodiments, the method may include obtaining sensor data from aone or more sensors disposed on a retail shelf between the retail shelfand one or more products placed on the retail shelf. As discussed above,a shelf (e.g., 850) associated with retail store (e.g., 105A, 105B,105C, etc.) may include one or more sensors (e.g., 851A, 851B, etc.)disposed between one or more products (e.g., 803A, 803B, 853A, 1210,1212, 1214, etc.). The one or more sensors (e.g., 851A, 851B, etc.) maybe configured to detect one or more parameters such as a position orchange of position of one or more products (e.g., 803A, 803B, 853A,1210, 1212, 1214, etc.) on the shelf 850. It is also contemplated thatin some embodiments the one or more sensors (e.g., 851A, 851B, etc.) maybe configured to measure a pressure being exerted on shelf 850 and/or aweight of shelf 850 to detect whether one or more products (e.g., 803A,803B, 853A, 1210, 1212, 1214, etc.) have been removed from shelf 850 byshopper 1202 or replaced on shelf 850 by shopper 1202. For example,processing device 202 may receive signals from a weight sensorpositioned on shelf 850 in retail store (e.g., 105A, 105B, 105C, etc.).Processing device 202 may determine that a product (e.g., 803A, 803B,853A, 1210, 1212, 1214, etc.) has been removed from shelf 850 orreturned to shelf 850 based on a change in weight detected by the weightsensor. By way of another example, processing device 202 may receivesignals from a pressure sensor positioned on shelf 850 in retail store(e.g., 105A, 105B, 105C, etc.). Processing device may determine that oneor more products have been removed from shelf 850 or returned to shelf850 based on a change in pressure detected by the pressure sensor. Asanother example, processing device 220 may receive signals from a touchsensor positioned on shelf 850 in retail store (e.g., 105A, 105B, 105C,etc.). Processing device 202 may determine that one or more productshave been removed from shelf 850 or returned to shelf 850 based onsignals received from the touch sensor. In another example, shelf 850 ora location in a vicinity of shelf 850 in retail store (e.g., 105A, 105B,105C, etc.) may be equipped with a light sensor. Processing device 202may determine that one or more products have been removed from shelf 850or returned to shelf 850 based on signals received from the lightsensor. As also discussed above, it is contemplated that in someembodiments the one or more sensors may measure other parameters such asresistance, capacitance, inductance, reflectance, emittance, etc., basedon a proximity of the one or more sensors with the one or more productsto determine whether a product (e.g., 803A, 803B, 853A, 1210, 1212,1214, etc.) has been removed from shelf 850 by shopper 1202 or returnedto shelf 850 by shopper 1202.

In some embodiments, the at least one product interaction event may bedetected based on analysis of the image data and the sensor data. Asdiscussed above, processing device 202 may detect whether a shopper hastaken an action associated with the product (e.g., interacted with theproduct) based on analysis of image data received from one or more imagesensors 310. As also discussed above, processing device 202 may detectwhether the shopper has taken an action associated with a product basedon signals received from one or more sensors (e.g., 851A, 851B, etc.)associated with a shelf (e.g., 850) in retail store (e.g., 105A, 105B,105C, etc.) It is also contemplated that in some embodiments, processingdevice 202 may determine whether a shopper (e.g., 1202) has interactedwith a product (e.g., 803A, 803B, 853A, 1210, 1212, 1214, etc.) locatedon shelf 850 based on an analysis of both the image data received fromone or more image sensors 310 and the sensor data received from one ormore sensors (e.g., 851A, 851B, etc.) associated with shelf 850. Forexamples, processing device 202 may determine that a product (e.g.,803A, 803B, 853A, 1210, 1212, 1214, etc.) has been removed from shelf850 based on an analysis of the image data obtained from image sensors310. Processing device 202 may confirm that the product (e.g., 803A,803B, 853A, 1210, 1212, 1214, etc.) has been removed from shelf 850 bydetermining whether there has been a change in a weight of shelf 850based on sensor data received from sensors (e.g., 851A, 851B, etc.). Byway of another example, in some situations, processing device 202 may beunable to determine whether a product (e.g., 803A, 803B, 853A, 1210,1212, 1214, etc.) has been removed from or returned to shelf 850 basedsolely on analysis of the image data. This may occur, for example,because another shopper (e.g., 1204, see FIG. 12B) or object may bepartially or fully occluding shopper 1202 in the image data whileshopper 1202 is interacting with a product (e.g., 803A, 803B, 853A,1210, 1212, 1214, etc.). In such cases, processing device 202 mayadditionally or alternatively rely on sensor data obtained from sensors(e.g., 851A, 851B, etc.) to determine whether shopper 1202 has removed aproduct from a shelf or returned a product to the shelf (e.g.,interacted with a product). Thus, various combinations of image data andsensor data may be used to determine whether a product interaction event(e.g., action by a shopper relative to a product in the store) hasoccurred.

In some embodiments, the at least one shopper may include a plurality ofshoppers, and wherein identifying the at least one shopper at the one ormore locations of the retail store may include determining an individualpath for each of the plurality of shoppers in the retail store. It iscontemplated that there may be more than one shopper present in a retailstore (e.g., 105A, 105B, 105C, etc.) at any given time. For example, asillustrated in FIG. 12B, shoppers 1202, 1204 may be present in theretail store. To identify a shopper (e.g., shopper 1202), it may benecessary to analyze images of shopper 1202 taken in different locationswithin the retail store (e.g., 105A, 105B, 105C, etc.). This may occur,for example, because image data obtained by image sensors 310 in onelocation of the store may not have sufficient information to identifyshopper 1202. For example, FIG. 12C illustrates a top view of anexemplary retail store 105. As illustrated in FIG. 12C, retail store 105may include checkout area 1252, aisles 1254, 1256, 1258, 1260, 1262,1264, etc. Shopper 1202 may interact with a product (e.g., 803A, 803B,853A, 1210, 1212, 1214, etc.) located on shelf 850 at location A (seeFIG. 12C) of a retail store (e.g., 105A, 105B, 105C, etc.). However, aface of shopper 1202 at location A may be partially or fully occluded inthe image data associated with location A, for example, due to thepresence of another shopper 1204, a shelf, or another object next toshopper 1202. Thus, it may not be possible to identify shopper 1202associated with the product interaction event (e.g., interaction ofshopper 1202 with a product) that may have occurred at location A.However, image data obtained from a different location (e.g., locationB) in the store may include a clearer or better image of shopper 1202.To identify shopper 1202 using the image data from a different location(e.g., locations A and B), it may be necessary to ensure the image dataat the two locations A and B corresponds to the same shopper 1202. Oneway of doing this may be to determine a path 1230 of shopper 1202 asshopper 1202 travels around store 150 and relating the two locations Aand B with path 1230 taken by shopper 1202. That is, it may be possibleto use image data at location B to identify shopper 1202 associated witha product interaction event at location A when locations A and B bothlie on path 1230 of shopper 1202 through the retail store 105.

In some embodiments, the individual path determined for each of theplurality of shoppers may be used in detecting the at least one productinteraction event. For example, as discussed above a shopper (e.g.,1202) may interact with a product (e.g., 803A, 803B, 853A, 1210, 1212,1214, etc.) located on shelf 850 at location A of a retail store (e.g.,105A, 105B, 105C, etc.). Processing device 202 may determine, forexample, based on analysis of image data obtained from image sensors 310and associated with location A that shopper 1202 has removed a product(e.g., 1212) from shelf 850 at location A. However, the removed product1212 may be occluded by shopper 1202, by another shopper 1204, oranother object located in the store 105. As a result, the image dataassociated with location A may be insufficient to identify product 1212that shopper 1202 may have a removed from shelf 850 at location A.However, as shopper 1202 travels through retail store 105 along path1230, image data of shopping cart 1220 associated with shopper 1202 maybe obtained at location B, and the image data associated with location Bmay allow processing device 202 to identify the previously unidentifiedproduct 1212 that shopper 1202 may have removed from shelf 850 atlocation A and placed in shopping cart 1220. Processing device 202 maybe configured to determine path 1230 of shopper 1202 from location A tolocation be in the store to be able to associate the product identifierusing the image data at location B. Processing device 202 may also beconfigured to use the determined path 1230 to identify shopper 1202 atlocations A and B. Further, processing device 202 may be configured toidentify a product interaction event (e.g., removal of product 1212 fromshelf 850) at location A based on analysis of image data at location Bon path 1230.

In some embodiments, the at least one product interaction event may bedetected based on a plurality of products that the at least one shopperis expected to buy. It is contemplated that in some embodiments,information associated with one or more products previously purchased bya shopper (e.g., 1202) may be stored in database 140. For example, whenshopper 1202 visits a retail store (e.g., 105B) and purchases one ormore products (e.g., 803A, 803B, 853A, 1210, 1212, 1214, etc.), a listof the one or more products purchased by shopper 1202 may be stored indatabase 140. It is contemplated that when shopper 1202 subsequentlyenters a retail store (e.g., 105B), processing device 202 may be able toaccess the list of previously purchases products associated with shopper1202 from database 140. Processing device 202 may also be configured toidentify the one or more products (e.g., 803A, 803B, 853A, 1210, 1212,1214, etc.) that shopper 1202 may have previously purchased at retailstore 105B. During the subsequent visit of shopper 1202 to retail store105B, the shopper may be expected to purchase one or more products fromthe list of previously purchased products. During the subsequent visitof shopper 1202 to retail store 105B, processing device 202 may detect aproduct interaction event based on an analysis of image data obtained byone or more image sensors 310, and/or based on a sensor data obtainedfrom one or more sensors 851A, 851B. Processing device 202 may identifythe product (e.g., 1204) associated with the product interaction eventbased on the list of previous purchases retrieved from database 140 andinformation regarding the retail store location (e.g., particular shelf850). For example, analysis of the image data may indicate that shopper1202 is associated with a product interaction event at a particularshelf 1254 (e.g., shelf that carries bread). Furthermore, processingdevice 202 may determine from the list of previous purchases retrievedfrom database 140 that shopper 1202 has previously purchased bread atretail store 105B. Processing device 202 may then associate the productinteraction event shelf 1254 with removal of a product (e.g., bread)based on the list of previous purchases associated with shopper 1202.

In some embodiments, the method may include determining whether the atleast one shopper is eligible for frictionless checkout based on thedetected at least one product interaction event. Many different criteriamay be used by processing device 202 to determine whether a shopper(e.g., 1202, 1204, etc.) is eligible for frictionless checkout based ona detected product interaction event. Some examples of these criteriaare provided below. It should be understood however that these examplesare nonlimiting and that many other criteria may be used determinewhether a shopper (e.g., 1202, 1204, etc.) is eligible for frictionlesscheckout. In some embodiments, processing device 202 may determine thata shopper (e.g., 1204) is ineligible for frictionless checkout when aproduct interaction event associated with shopper 1204 is associatedwith an unidentified product. For example, processing device 202 maydetect a product interaction event in which shopper 1204 removes aproduct (e.g., 1214) from shelf 850 or returns product 1214 to shelf 850in retail store 105. However, image data and/or sensor data associatedwith the product interaction event may be insufficient to identifyproduct 1214. As a result, processing device 202 may associate theproduct interaction event with an unidentified product. Because product1214 is unidentified based on analysis of the image and/or sensor data,processing device 202 may designate shopper 1204 as being ineligible forfrictionless checkout.

In some embodiments, determining whether the at least one shopper iseligible for frictionless checkout may be based on whether the at leastone shopper is detected removing or selecting a product from a shelfthat may be designated as ineligible for frictionless checkout. Forexample, a retailer may designate certain products as being ineligiblefor frictionless checkout. Such products may include, for example,high-priced items (e.g., aged bottle of wine, premium olive oil, caviar,etc.), items that may be available only in a limited quantity (e.g.,particular brand or vintage of wine, particular brand of a product,etc.), items that may be age restricted (e.g., alcohol, tobacco, etc.),items requiring additional information or input (e.g., gift cards ofvariable monetary value), or the like. It is contemplated that retailermay designate shelf 850 carrying such products as constituting a shelfthat is ineligible for frictionless checkout. As discussed above,processing device 202 may detect a product interaction event when, forexample, a shopper (e.g., 1202) removes a product (e.g., 803A, 803B,853A, 1210, 1212, 1214, etc.) from a shelf in a retail location. Whenprocessing device 202 determines that shopper 1202 removed a productfrom shelf 850 that has been designated as ineligible for frictionlesscheckout, processing device 202 may determine that shopper 1202 isineligible for frictionless checkout.

By way of another example, a particular shelf (e.g., 1256) in a retailstore (e.g., 105A, 105B, 105C, etc.) may include one or more displaysassociated with one or more services (e.g., free delivery, opening a newcredit card account, vacation deals, home cleaning services, gardeningservices, etc.). It is contemplated that retailer may designate shelf1256 associated with one or more services as being ineligible forfrictionless checkout. As discussed above, processing device 202 maydetect a product interaction event when, for example, shopper 1202selects materials associated with the one or more services from shelf1256. When processing device 202 determines that shopper 1202 hasselected a service from a shelf 1256 that has been designated asineligible for frictionless checkout, processing device 202 maydetermine that shopper 1202 is also ineligible for frictionlesscheckout.

By way of another example, a particular shelf 1256 retail store mayinclude one or more interactive displays (e.g., touch screen device,tablet, etc.) that may allow shopper 1202 to select one or more productsand/or one or more services. It is contemplated that a retailer maydesignate this particular shelf 1256 associated with the one or moreinteractive displays as being ineligible for frictionless checkout. Asdiscussed above, processing device 202 may detect a product interactionevent when, for example, shopper 1202 selects one or more items from theone or more interactive displays on shelf 1256. When processing device202 determines that shopper 1202 has selected one or more items from theinteractive displays on a shelf 1256 that has been designated as beingineligible for frictionless checkout, processing device 202 maydetermine that shopper 1202 is also ineligible for frictionlesscheckout.

In some embodiments, determining whether the at least one shopper iseligible for frictionless checkout may be based on at least oneindicator of a degree of ambiguity associated with the detected at leastone product interaction event. In some embodiments, the at least oneindicator of the degree of ambiguity may be determined based on theimage data. As discussed above, processing device 202 may detect one ormore product interaction events based on an analysis of image dataobtained by the one or more image sensors 310. It is contemplated thatin some instances, processing device 202 may not be able to identifyeither the shopper or the product being removed from shelf 850 or beingreturned to shelf 850, or both because of the quality of the image data.For example, in some instances images obtained by the one or moresensors 310 may be too dark because of insufficient light. As anotherexample, portions of an image of shopper 1202 and/or a product (e.g.,803A, 803B, 853A, 1210, 1212, 1214, etc.) may be occluded by anothershopper 1204, and/or another object. By way of another example, allimage of a product (e.g., 803A, 803B, 853A, 1210, 1212, 1214, etc.) maybe blurry or out of focus making it difficult to, for example, read alabel on the product using optical character recognition techniques. Ineach of the above-described examples, processing device 202 may beunable to identify shopper 1202 and/or a product (e.g., 803A, 803B,853A, 1210, 1212, 1214, etc.) associated with a product interactionevent. Processing device 202 may be configured to determine an indicatorof the degree of ambiguity associated with the product interaction eventwhen processing device 202 is unable to identify shopper 1202 and/or aproduct (e.g., 803A, 803B, 853A, 1210, 1212, 1214, etc.) associated withthe product interaction event. By way of example, the indicator may be anumerical value ranging between a minimum and maximum value, with thevalue indicating a degree of ambiguity. As another example, theindicator may be in the form of text (e.g., Low, Medium, High, etc.)indicating a degree of ambiguity. By way of example, processing device202 may be configured to identify shopper 1202 and/or a product (e.g.,803A, 803B, 853A, 1210, 1212, 1214, etc.) by comparing the image dataobtained from the one or more image sensors 310 with one or morereference images of shopper 1202 and/or a product (e.g., 803A, 803B,853A, 1210, 1212, 1214, etc.). Processing device 202 may be configuredto determine the indicator of ambiguity based on, for example, a degreeof similarity between the image data and the reference image of theshopper 1202 and/or a product (e.g., 803A, 803B, 853A, 1210, 1212, 1214,etc.). It is also contemplated that processing device 202 may executeone or more mathematical or statistical algorithms or other models todetermine the indicator of ambiguity.

In some embodiments, the at least one indicator of the degree ofambiguity may be determined based on the image data and on data capturedusing at least one sensor disposed on a surface of a retail shelf. Asdiscussed above, in some instances, processing device 202 may use acombination of analyses of image data obtained from the one or moreimage sensors 310 and sensor data obtained from one or more sensors(e.g., 851A, 851B) to identify one or more products (e.g., 803A, 803B,853A, 1210, 1212, 1214, etc.) involved in a product interaction event.Processing device 202 may be configured to determine an indicator ofambiguity when, for example, processing device 202 is unable to identifya product (e.g., 803A, 803B, 853A, 1210, 1212, 1214, etc.) removed fromor returned to shelf 850 by shopper 1202 based on analysis of both theimage data and the sensor data. For example, processor 202 may beconfigured to identify a product (e.g., 803A, 803B, 853A, 1210, 1212,1214, etc.) removed from or returned to shelf 850 by comparing a changein weight or pressure detected by, for example, sensors 851A, 851B witha reference weight or pressure associated with the product. Processingdevice 202 may be configured to determine an indicator of ambiguitybased on a difference between the change in weight and the referenceweight, or the change in pressure and the reference pressure. It iscontemplated that processing device 202 may execute various mathematicalor statistical algorithms or other models to determine the indicator ofambiguity based on analysis of both the image data and the sensor dataassociated with a product interaction event. It is also contemplatedthat in some embodiments, processing device 202 may use mathematicaland/or statistical algorithms or other models to combine the indicatorsof ambiguity obtained based on analysis of the image data and analysisof the sensor data.

It is contemplated that processing device 202 may determine whethershopper 1202 is eligible for frictionless checkout based on thedetermined indicator of ambiguity. For example, processing device 202may compare the determined indicator of ambiguity with a thresholdindicator of ambiguity. Processing device 202 may be configured todetermine that the shopper is ineligible for frictionless checkout whenthe determined indicator of ambiguity is greater than or equal to thethreshold indicator of ambiguity. On the other hand, processing device202 may be configured to determine that the shopper is eligible forfrictionless checkout, when the determine indicator of ambiguity is lessthan the threshold indicator of ambiguity.

In some embodiments, the determination that the at least one shopper isineligible for frictionless checkout may be based on a determination ofa number of ambiguous events among the detected at least one productinteraction event. In addition to determining an indicator of ambiguity,processing device 202 may be configured to determine a number ofproducts interaction events that may ambiguous (e.g., that may have anindicator of ambiguity greater than or equal to a predeterminedthreshold indicator of ambiguity). For example, processing device 202may compare the determined indicator of ambiguity with the thresholdindicator of ambiguity and identify that product interaction events areambiguous when indicators of ambiguity associated with those productinteraction events exceed the predetermined indicator of ambiguity.Processing device 202 may also be configured to compare a total numberof ambiguous product interaction events with the total number ofdetected product interaction events. In some embodiments, the at leastone shopper may be determined to be ineligible for frictionless checkoutif the number of ambiguous events exceeds a predetermined threshold. Forexample, processing device 202 may be configured identify shopper 1202as being ineligible for frictionless checkout when the number ofambiguous events exceeds the predetermined threshold number of ambiguousevents. In some embodiments, the predetermined threshold may be based ona total number of the detected product interaction events. For example,processing device 202 may be configured to identify shopper 1202 asbeing eligible or ineligible for frictionless checkout based on a ratioof a number of ambiguous events and the total number of productinteraction events. By way of example, when the percentage of ambiguousproduct interaction events is greater than 50% (e.g., when a ratio ofthe total number of ambiguous product interaction events to the totalnumber of detected product interaction events is greater than 0.5),processing device 202 may be configured to determine that the shopper1202 is ineligible for frictionless checkout. On the other hand, whenthe percentage of ambiguous product interaction events is relatively low(e.g., 0-0.3 or less than 30%), processing device 202 may be configuredto determine that shopper 1202 is eligible for fictionalize checkout.

In some embodiments, the determination that the at least one shopper isineligible for frictionless checkout may be based on a determination ofa product value associated with one or more ambiguous events among thedetected at least one product interaction event. It is contemplated thatin some embodiments, shopper 1202 may be deemed ineligible forfrictionless checkout when, for example, a product interaction eventassociated with a high-value product may have been determined to beambiguous. By way of example, processing device 202 may determine that aproduct interaction event in which shopper 1202 removes a high-valueproduct from shelf 1256 is ambiguous. In response, processing device 202may be configured to determine that shopper 1202 is ineligible forfrictionless checkout.

In some embodiments, the method may include causing an ambiguityresolution action in response to a detection of at least one ambiguousevent among the detected at least one product interaction event. In someembodiments, when processing device 202 identifies a product interactionevent is ambiguous, processing device 202 may initiate an ambiguityresolution action. For example, processing device 202 may send aninstruction to a device associated with a store associate, asking thestore associate to determine whether shopper 1202 removed a product(e.g., 803A, 803B, 853A, 1210, 1212, 1214, etc.) from a shelf (e.g.,1258) during the ambiguous product interaction event. The storeassociate may make the determination by visually inspecting products inshopping cart 1220 of shopper 1202, or by directly interacting withshopper 1202 and asking shopper 1202 whether he or she removed a productassociated with the ambiguous product interaction event. In someembodiments, the store associate may direct shopper 1202 to a checkoutaisle to perform the inspection. Based on the inspection or interactionwith shopper 1202, store associate may alter the status of the ambiguousproduct interaction event. For example, after confirming the shopper1202 removed a product (e.g., 1214) from shelf 1258, the store associateand/or processing device 202 may change the status of the ambiguousproduct interaction event to an unambiguous product interaction event.

In some embodiments, the method may include causing an eligibilitystatus for frictionless checkout for the at least one shopper to berestored based on data associated with a completion of the ambiguityresolution action. For example, when a previously marked ambiguousproduct interaction event is updated and deemed an unambiguous productinteraction event, the eligibility status of and associated shopper 1202may be changed. By way of example, when processing device 202 determinesthe product interaction event to be ambiguous, processing device 202 maydeem an associated shopper 1202 as being ineligible for frictionlesscheckout. However, when, for example, a store associate revises thestatus of the ambiguous product interaction event and marks it as notbeing ambiguous, processing device 202 may revise the status of theassociated shopper 1202 from being ineligible for frictionless checkoutto being eligible for frictionless checkout.

In some embodiments, determining whether the at least one shopper iseligible for frictionless checkout may include determining an indicatorof a confidence level associated with each detected product interactionevent. For example, as discussed above, processing device 202 mayanalyze image data obtained from the one or more image sensors 310 todetermine the occurrence of a product interaction event (e.g., removalof a product from a shelf, return of a product to a shelf, etc.). Asalso discussed above, processing device 202 may additionally oralternatively analyze sensor data obtained from the one or more sensors851A, 851B to determine the occurrence of a product interaction event.Processing device 202 may be configured to determine a confidence levelassociated with a detected product interaction event. For example,processing device 202 may assign a high confidence level (e.g. 80% to100%) when there is a high likelihood that a product interaction eventhas occurred, that is, when there is a high likelihood that shopper1202, for example, has removed a product (e.g., 803A, 803B, 853A, 1210,1212, 1214, etc.) from a shelf (e.g., 850, 1254, 1256, etc.) or returneda product (e.g., 803A, 803B, 853A, 1210, 1212, 1214, etc.) to a shelf(e.g., 850, 1254, 1256, etc.). However, in some instances, processingdevice 202 may not be able to determine whether shopper 1202 has removeda product from or returned a product to a shelf (e.g., 850, 1254, 1256,etc.). This may occur for instance when an image of shopper 1202 and/orproduct (e.g., 803A, 803B, 853A, 1210, 1212, 1214, etc.) is occluded byanother shopper 1204 or another object in the retail store. Additionallyor alternatively, this may occur when for example more than one shopper1202, 1204 interacts with products on a shelf. FIG. 12B illustrates asituation where, for example, both shoppers 1202 and 1204 removeproducts 1210 and 1212, respectively from shelves 850. As illustrated inFIG. 12B, camera 1270, including image sensor 310 may be located at oneend of shelves 850. As a result, in the image data obtained by camera1270, an image of shopper 1202 may be occluded by an image of shopper1204. Additionally or alternatively, an image of shopper 1202's hand1206 removing product 1210 may be occluded by image of shopper 1204'shand 1208, which may be removing product 1212 from shelves 850. In thissituation, processing device 202 may not be able to determine which ofshoppers 1202 and/or 1204 removed product 1210 from shelf 850. Whenprocessing device 202 determines that there is a lower likelihood that aproduct interaction event has occurred (because is it not clear whichshopper removed product 1210), processing device 202 may be configuredto assign a low confidence level (e.g., 0%-20%) to the detected productinteraction event.

In some embodiments, a determination that the at least one shopper isineligible for frictionless checkout may be based on whether theconfidence level associated with the at least one product interactionevent is below a predetermined threshold. It is contemplated thatprocessing device 202 may determine whether shopper 1202 is eligible orineligible for frictionless checkout based on a confidence levelassociated with a product interaction event associated with shopper1202. For example, shopper 1202 may be deemed eligible for frictionlesscheckout, when processing device 202 has assigned a high confidencelevel (e.g., 80%-100%) to a product interaction event. On the otherhand, shopper 1202 may be deemed ineligible for frictionless checkoutwhen a confidence level associated with the product interaction event islow (e.g., 0% to 20%). By way of another example, when a productinteraction event is associated with a high-value product, the reversemay be true. That is, when a confidence level associated with a productinteraction event associated with a high-value product is high (e.g.,80%-100%), processing device 202 may determine that shopper 1202 isineligible for frictionless checkout. On the other hand, when aconfidence level associated with a product interaction event related toa high-value product is low (e.g., 0% to 20%), processing device 202 maydetermine that shopper 1202 is eligible for frictionless checkout.

In some embodiments, determining the indicator of the confidence levelfor each detected product interaction event may depend on a distancebetween a detected additional shopper and the at least one shopper whenthe at least one shopper removes a product from a shelf or returns aproduct to the shelf. As discussed above, then may be plurality ofshoppers (e.g., 1202, 1204) present in a retail store. In particular, insome instances, there may be more than one shopper 1202, 1204 presentnear a particular shelf 850. FIG. 12D illustrates shoppers 1202, 1204present near shelf 850. Processing device 202 may detect the occurrenceof a product interaction event based on image data associated with shelf850. However because the presence or more than one shopper (e.g.,shopper 1202, shopper 1204, etc.) near shelf 850, processing device 202may not be able to identify whether shopper 1202 or shopper 1204 wasresponsible for removing a product from or returning a product to shelf850. It is contemplated that processing device 202 may be able todetermine which of shoppers 1202 or 1204 interacted with the productbased on the distance between shoppers 1202 or 1204 and shelf 850, ascompared to a distance between shopper 1202 and shopper 1204. Forexample, as illustrated in FIG. 12D in one instance, shopper 1202 may bepositioned at a distance L₁ relative to product 1210 whereas, shopper1204 may be positioned at a distance L₂ from shopper 1202. Thus, shopper1204 may be positioned at a distance L₁+L₂ from product 1210, which maybe larger than distance L₁ between shopper 1202 and product 1210. Inthis instance, processing device 202 may identify shopper 1202 as beingassociated with the product interaction event. Processing device 202 mayassign a confidence level to the product interaction event based on thedistance L₂ between shopper 1202 and shopper 1204. For example, when adistance L₂ between shopper 1202 and shopper 1204 is relatively small,processing device 202 may assign a low confidence level (e.g., 0%-20%)to the product interaction event. This is because when the distance L₂between shopper 1202 and shopper 1204 is low, it may be difficult todetermine which of shoppers 1202 or 1204 removed product 1210 from shelf850 or returned product 1210 to shelf 850. In contrast, when distance L₂between shopper 1202 and shopper 1204 is relatively large, processingdevice 202 may assign a high confidence level (e.g., 80%-100%) to theproduct interaction event. This is because when the distance L₂ betweenhopper 1202 and shopper 1204 is relatively large, it may be possible toidentify with more certainty whether shopper 1202 or shopper 1204 wasassociated with the product interaction event.

In some embodiments, the method may include updating the confidencelevel of a particular product interaction event after receivingadditional input indicative of products purchased by at least oneadditional shopper. As discussed above, in some instances processingdevice 202 may assign a low confidence level (e.g., 0%-20%) to a productinteraction event because of the uncertainty associated with determiningwhich of, for example, shoppers 1202 or 1204 may be associated with theproduct interaction event. It is contemplated, however, that as shoppers1202 and 1204 move around the retail store 105 one or more image sensors310 may be able to obtain additional image data associated with each ofshoppers 1202, 1204. In some instances, processing device 202 may beable to determine, for example, that shopping cart 1220 associated withshopper 1202 includes a product 1210 associated with a productinteraction event that has been previously assigned a low confidencelevel. Based on the additional image data, however, processing device202 may update or modify the confidence level associated with thatinteraction event. For example, when processing device 202 determinesbased on the subsequent image data that product 1210 is associated withfor example, shopping cart 1220 of shopper 1202, processing device 202may update the confidence level associated with the product interactionevent by increasing the confidence level to a high confidence level.

In some embodiments, the method may include obtaining cart dataindicative of an actual plurality of products within a cart of aparticular shopper. For example, as shopper 1202 moves around a retailstore (e.g., 105A, 105B, 105C, etc.), one or more sensors 310 may beconfigured to obtain image data including images of for example shoppingcart 1220, including the one or more products (e.g., 803A, 803B, 853A,1210, 1212, 1214, etc.) that may have been purchased by shopper 1202.Processing device 202 may perform image analysis on the received imagedata to identify the products (e.g., 803A, 803B, 853A, 1210, 1212, 1214,etc.) that may be present in shopping cart 1220. Processing device 202may also be configured to determine a number of each identified productpresent in shopping cart 1220 and/or a total number of products presentin shopping cart 1220 based on analysis of the image data.

In some embodiments the method may include determining, based onanalysis of the detected at least one product interaction event, anexpected plurality of products within the cart of the particularshopper. As discussed above, processing device 202 may analyze imagedata and/or sensor data associated with each of one or more productinteraction events. Processing device 202 may be configured to determinewhether one or more products (e.g., 803A, 803B, 853A, 1210, 1212, 1214,etc.) were removed from one or more shelves 850 and/or returned to theone or more shelves 850 based on the analysis of the image data and/orsensor data. Processing device 202 may also be configured to identifythe one or more products (e.g., 803A, 803B, 853A, 1210, 1212, 1214,etc.) that may have been removed from shelf 850 during the one or moredetected product interaction events. Based on the identification of theone or more products, processing device 202 may be configured todetermine a number of each identified product (e.g., 803A, 803B, 853A,1210, 1212, 1214, etc.) and/or a total number of products that may havebeen removed by a particular shopper (e.g., 1202) during the one or moredetected product interaction events. Thus processing device 202 may beconfigured to determine an expected number of products that should bepresent in shopping cart 1220 associated with shopper 1202 based onanalysis of the image data and/or the sensor data associated with theone or more product interaction events.

In some embodiments, the method may include determining whether adiscrepancy exists between the actual plurality of products and theexpected plurality of products. For example, processing device 202 maycompare the actual number of products determined to be present inshopping cart 1220 associated with shopper 1202 with the expected numberof products for shopper 1202. In some embodiments, processing device mayalso be configured to compare a number of each identified productdetermined to be present in shopping cart 1220 associated with shopper1202 with the expected number of that identified product for thatshopper 1202. Processing device 202 may also be configured to determinea discrepancy (e.g., difference between the numbers of products presentin shopping cart 1220 associated with shopper 1202 and the expectednumbers of products for that shopper 1202). In some embodiments, themethod may include determining that the particular shopper is ineligiblefor frictionless checkout based on the determined discrepancy. It iscontemplated that processing device 202 may determine that shopper 1202is ineligible for frictionless checkout when processing device 202determines that the number of products present in shopping cart 1220associated with shopper 1202 is greater than a number of productsexpected to be in shopping cart 1220 based on analysis of the image dataand sensor data associated with one or more product interaction events.For example, in one instance processing device 202 may determine that anumber of products actually present in shopping cart 1220 associatedwith shopper 1202 is greater than an expected number of products forthat particular shopper 1202. Such a discrepancy may indicate that oneor more product interaction events may not have been captured in theimage data and/or sensor data, and/or may not have been detected byprocessing device 202. Processing device 202 may therefore determinethat shopper 1202 is ineligible for frictionless checkout.

In some embodiments, the at least one shopper may be determined to beineligible for frictionless checkout if the product value exceeds apredetermined threshold. For example, processing device 202 may comparea price (e.g., value) of a product (e.g., 803A, 803B, 853A, 1210, 1212,1214, etc.), which shopper 1202 may have removed from shelf 850, with apredetermined threshold price or value. Processing device 202 maydetermine that shopper 1202 is ineligible for frictionless checkout whenthe price of the product removed by shopper 1202 is greater than orequal to the predetermined threshold price or value. For example, asdiscussed above, the product removed by shopper 1202 may be ahigh-priced item and the retailer may want to ensure shopper 1202 makespayment for that high-priced item before leaving retail store 105. Insome embodiments, the predetermined threshold may be up to a selectedvalue for a single product. For example, the predetermined thresholdprice or value may be determined based on one product selected from theplurality of products that the shopper may have removed from one or moreshelves 850 during the shopper's visit to a retail store (e.g., 105A,105B, 105C, etc.). By way of example, the threshold price may bedetermined as a maximum price of a product already present in shoppingcart 1220 of shopper 1202. In some embodiments, the predeterminedthreshold may be up to a selected ratio of a total product valueassociated with the detected product interaction events. For example, insome embodiments, the threshold price or value may be based on a totalprice or value of all the products that the shopper may have removedfrom the one or more shelves 850 during the shopper's visit to a retailstore (e.g., 105A, 105B, 105C, etc.). Processing device 202 maycontinuously or periodically determine a total price or value of all theitems that the shopper may have removed from the one or more shelves850. Processing device 202 may determine the threshold price as being apredetermined percentage (e.g., 25%, 50%, etc.) or ratio (0.25, 0.5,etc.) of the total price. Processing device 202 may determine that theshopper is ineligible for frictionless checkout when a price of aproduct (e.g., 803A, 803B, 853A, 1210, 1212, 1214, etc.) removed fromshelf 850 by shopper 1202 is greater than the predetermined percentageof ratio of the total price of all the products in shopping cart 1220.For example, if the total price of the products in shopping cart 1220 isT and the predetermined ration is 0.25, then processing device 202 maydetermine that shopper 1202 is ineligible for frictionless checkout whenshopper 1202 removes a product having a price greater than 0.25T fromshelf 850.

In some embodiments, the method may include accessing a customer profileassociated with a particular shopper. In some embodiments, the methodmay include foregoing the delivery of the indicator that the particularshopper is ineligible for frictionless checkout based on informationassociated with the customer profile. As discussed above, it iscontemplated that server 135 and/or database 140 may store informationassociated with one or more shoppers 1202, 1204 in the form of customerprofiles. For example, a customer profile for shopper 1202 may includeidentification information of shopper 1202 (e.g., a name, anidentification number, an address, and telephone number, an emailaddress, a mailing address), and/or other information associated withshopper 1202. The other information may include, for example, shoppinghistory, including a list of products previously purchased by shopper1202, frequency of purchase of each of the products in the list, totalvalue of products purchased by shopper 1202 during each visit to aretail store or during a predetermined period of time, payment historyof shopper 1202, including information regarding on-time payments, latepayments, delinquent payments, etc. The other information may alsoinclude information regarding any charges that shopper 1202 may havecontested in the past, and/or other information associated with purchaseof products at the retail store by shopper 1202. It is contemplated thatin some embodiments, processing device 202 may determine that shopper1202 is eligible for frictionless checkout based on the informationincluded in the customer profile associated with shopper 1202.

In some embodiments, the information may indicate that the particularshopper is a trusted shopper. A trusted shopper as used in thisdisclosure may be determined based on information in the customerprofile that indicates, for example, that shopper 1202 has previouslyinformed the retail store 105 regarding errors in the price of productspreviously purchased by the shopper (e.g., under-charging shopper 1202),that shopper 1202 has paid for products purchased on time, and/or thatshopper 1202 has a good credit history, etc. It is to be understood thatthese criteria for defining a trusted shopper are exemplary andnonlimiting and that many these or other criteria may be usedindividually or in any combination to define a trusted shopper. It iscontemplated that processing device 202 may designate shopper 1202 asbeing eligible for frictionless checkout when the customer profileassociated with shopper 102 includes one or more items of informationindicating that the shopper is a trusted shopper.

In some embodiments, the information may indicate that the particularshopper is a returning customer. By way of another example, theinformation in a customer profile associated with shopper 1202 mayindicate that shopper 1202 has previously shopped at a particular retailstore (e.g., 105C). It also contemplated that in some embodiments thecustomer profile associated with shopper 1202 may include an indicatoror a flag indicating that shopper 1202 is a returning customer and haspreviously shopped at, for example, retail store 105C. Processing device202 may designate that shopper 1202 is eligible for frictionlesscheckout based on information in the customer profile, indicating thatshopper 1202 is a returning customer.

In some embodiments, the information indicates that the particularshopper does not have a history of ambiguous product interaction events.By way of another example, a customer profile associated with a shopper(e.g., 1202) may include information regarding prior ambiguous productinteraction events. Processing device 202 may determine whether a totalnumber of prior ambiguous product interaction events in a customerprofile for shopper 1202 is greater than or equal to a predeterminedthreshold number of ambiguous product interaction events. Processingdevice 202 may determine that shopper 1202 is eligible for frictionlesscheckout when the number of ambiguous product interaction events in thecustomer profile associated with shopper 1202 is less than thepredetermined threshold number of ambiguous product interaction events.

In some embodiments, the information may indicate that the particularshopper is not associated with prior fraudulent transactions. By way ofanother example, a customer profile may include information regardingprior purchases of one or more products from a retail store (e.g., 105A,105B, 105C, etc.) or returns of one or more products to the retailstore. The customer profile may also include information or anindication whether one or more of the prior purchases or returnsincluded fraudulent transactions (e.g., payments using a fake or stolencredit card account, returning a product different from that sold by theretail store, purchasing one or more products without paying for theproducts, etc.) Processing device 202 may determine that shopper (e.g.,1202) is ineligible for frictionless checkout when the customer profileassociated with shopper 1202 indicates that shopper 1202 previouslyengaged in one or more fraudulent transactions.

In some embodiments, the information may indicate that the particularshopper is a valuable customer. By way of another example, a customerprofile may include information indicating that a shopper (e.g., 1202)is a valuable customer. As used in this disclosure, a shopper may bedetermined to be a valuable customer based on the shopper's priorpurchase history. For example, shopper 1202 may be determined to be avaluable shopper when an amount of money spent by shopper 1202 at aparticular retail location (e.g., 105B) is greater than or equal to athreshold amount of money, or when the number of products purchased byshopper 1202 at retail location 105B is greater than or equal to athreshold number of products. In some embodiments, shopper 1202 may bedetermined to be a valuable shopper based on a frequency with shopper1202 makes purchases at retail store 105B. In other embodiments, shopper1202 may be determined to be a valuable shopper, for example, whenshopper 1202 frequently purchases high-value items. It is alsocontemplated that shopper 1202 may be determined to be a valuableshopper based on a combination of one or more of the above-identifiedfactors. It is to be understood that the disclosed criteria for defininga valuable shopper are exemplary and non-limiting and that many othercriteria may be used to define a valuable shopper.

In some embodiments, the method may include causing delivery of anindicator that the at least one shopper is ineligible for frictionlesscheckout in response to a determination that the at least one shopper isineligible for frictionless checkout. For example, processing device 202may generate an indicator, indicating whether a shopper is eligible orineligible for frictionless checkout. The indicator may be in the formof a numerical value, a textual message, and/or a symbol or image.Processing device 202 may also be configured to adjust a color, or font,and/or other display characteristics of the indicator. Processing device202 may be configured to transmit the indicator to a device associatedwith the retailer and/or with the shopper. In some embodiments, causingthe delivery of the indicator that the at least one shopper isineligible for frictionless checkout includes sending a notification toa wearable device associated with the at least one shopper. For example,processing device 202 may be configured to transmit the indicator to awearable device (e.g., a smartwatch, a smart glass, etc.) associatedwith the shopper. The indicator received from processing device 202 maybe displayed on a display associated with the wearable device. In someembodiments, causing the delivery of the indicator that the at least oneshopper is ineligible for frictionless checkout includes sending anotification to a mobile device associated with the at least oneshopper. It is contemplated that additionally or alternatively,processing device 202 may transmit the indicator to one or more mobiledevices (e.g., a smart form, a tablet computer, a laptop computer, etc.)associated with the shopper. FIG. 13A illustrates an exemplarysmartphone 1310 having a display 1320. As illustrated in FIG. 13A, anexemplary indicator including symbol 1330 and text 1340 (e.g.,INELIGIBLE FOR FRICTIONLESS CHECKOUT and/or PLEASE PROCEED TO CHECKOUTCOUNTER OR SELF CHECKOUT) may be displayed on display 1320. It is alsocontemplated that when a shopper (e.g., 1202, 1204, etc.) is determinedto be eligible for frictionless checkout, processing device 202 maycause the one or more indicator devices or display devices discussedabove to display an indicator, indicating that the shopper (e.g., 1202,1204, etc.) is eligible for frictionless checkout. For example, in thiscase, symbol 1330 may be replaced by a check mark and text 1340 mayinstead display “ELIGIBLE FOR FRICTIONLESS CHECKOUT” and/or “YOU MAYEXIT THE STORE WHENEVER YOU ARE READY.” It is to be understood that thesymbols and text discussed above are exemplary and non-limiting and theindicator may additionally or alternatively include other symbols, text,and/or graphical elements.

In some embodiments, causing the delivery of the indicator that the atleast one shopper is ineligible for frictionless checkout includescausing a notification to be generated by a shopping cart associatedwith the at least one shopper. It is also contemplated that in someembodiments a shopping cart (e.g., 1230) being used by a shopper (e.g.,1202) may be equipped with an indicator or display device, and displaydevice on the shopping cart may be configured to display an indicator,indicating whether the shopper is eligible or ineligible forfrictionless checkout. For example, FIG. 13B illustrates shopper 1202adjacent shelves 850. As illustrated in FIG. 13B, shopping cart 1220 ofshopper 1202 may include indicator or display device 1350. Processingdevice 202 may be configured to transmit an indicator (e.g., 1330, 1340,etc.) to display device 1350 on the shopping cart 1220. Processingdevice 202 may also be configured to transmit instructions to displaydevice 1350 on the shopping cart 1220 to display the indicator (e.g.,1330, 1340, etc.). In some embodiments, processing device 202 mayadditionally or alternatively be configured to transmit an indicator(e.g., 1330, 1340, etc.) to display device 1360 that may be affixed toone or more shelves 850. Processing device 202 may also be configured totransmit instructions to display device 1360 on affixed to one or moreshelves 850 to display the indicator (e.g., 1330, 1340, etc.).

In some embodiments, causing a delivery of the indicator that the atleast one shopper is ineligible for frictionless checkout includessending a notification to a computing device associated with a storeassociate of the retail store. It is further contemplated thatadditionally or alternatively, processing device 202 may be configuredto transmit the indicator (e.g., 1330, 1340, etc.), indicating whether ashopper (e.g., 1202, 1204, etc.) is eligible for frictionless checkout,to a device associated with the retailer. For example, processing device202 may transmit the indicator (e.g., 1330, 1340, etc.) to one or moreof a mobile phone, a tablet computer, a laptop computer, a desktopcomputer, a smartwatch, etc., associated with a store associate or otheremployee of the retailer.

In some embodiments, the delivery of the indicator that the at least oneshopper is ineligible for frictionless checkout occurs after the atleast one shopper enters a checkout area of the retail store. Processingdevice 202 may transmit the indicator (e.g., 1330, 1340, etc.),indicating whether a shopper (e.g., 1202, 1204) is ineligible forfrictionless checkout at any time after determining that the shopper isineligible for frictionless checkout. For example, processing device 202may transmit the indicator during the time the shopper (e.g., 1202,1204) travels around a retail store (e.g., 105A, 105B, 105C), and/orwhen the shopper (e.g., 1202, 1204) approaches a checkout counter (e.g.,1252) associated with the retail store (e.g., 105A, 105B, 105C).

FIG. 14 is a flowchart showing an exemplary process 1400 for determiningwhether shoppers are eligible for frictionless checkout. Process 1400may be performed by one or more processing devices associated withapparatus server 135, such as processing device 202.

In step 1402, process 1400 may include obtaining image data capturedusing one or more image sensors positioned in a retail store. Forexample, as discussed above, a retail store (e.g., 105A, 105B, 105C,etc., see FIG. 1 ) may include one or more capturing devices 125configured to capture one or more images. One or more of capturingdevices 125 may include one or more image sensors 310 that may beconfigured to capture images of one or more persons (e.g., shoppers,store associates, etc.), one or more shelves 350, one or more items803A, 803B, 853A, etc. on shelves 350, and/or other objects (e.g.,shopping carts, checkout counters, walls, columns, poles, aisles,pathways between aisles), etc. The images may be in the form of imagedata, which may include, for example, pixel data streams, digitalimages, digital video streams, data derived from captured images, etc.

In step 1404, process 1400 may include analyzing the image data toidentify at least one shopper at one or more locations of the retailstore. For example, processing device 202 may analyze the image dataobtained by the one or more image sensors 310 to identify one or morepersons or objects in the image data. It is contemplated that processingdevice 202 may use any suitable image analysis technique, for example,including one or more of object recognition, object detection, imagesegmentation, feature extraction, optical character recognition (OCR),object-based image analysis, shape region techniques, edge detectiontechniques, pixel-based detection, artificial neural networks,convolutional neural networks, etc., to identify one or more persons orobjects in the image data. It is further contemplated that processingdevice 202 may access one or more databases 140 to retrieve one or morereference images of likenesses of one or more persons. Further,processing device 202 may use one or more of the image analysistechniques discussed above to compare the images retrieved from database140 with the image data received from the one or more image sensors 310to recognize the likeness of one or more shoppers in the image data. Itis also contemplated that processing device 202 may retrieve otheridentifying information (e.g., name, customers ID, account number,telephone number, etc.) associated with the images retrieved fromdatabase 140 based on, for example, profiles of the one or more shoppersstored in database 140. In some embodiments, processing device 202 mayalso be configured to employ machine learning algorithms or artificialneural networks to recognize and identify one or more shoppers in theimage data obtained by image sensors 310.

In step 1406, process 1400 may include detecting, based on the analysisof the image data, at least one product interaction event associatedwith an action of the at least one shopper at the one or more locationsof the retail store. For example, as a shopper passes through the retailstore, a shopper may interact with one or more products located in thestore by performing one or more actions. For example, as illustrated inFIG. 12A, shopper 1202 may be standing near shelf 850 that may becarrying products 1210, 1212, 1214, etc. Shopper 1202 may have shoppingcart 1220. As illustrated in FIG. 12A, shopper 1202 may interact withthe one or more products 1210, 1212, 1214, etc., by picking up product1210 and removing product 1210 from shelf 850. Additionally oralternatively, shopper 1202 may interact with the one or more products(e.g., 1210, 1212, 1214, etc.) by inspect the product, positioning theproduct in various orientations, returning the product to shelf 850,placing the product in shopping cart 1220, removing the product fromshopping cart 1220, and/or moving the product from one location toanother. Some other examples are described below, for example inrelation to FIGS. 24-26 .

In step 1408, process 1400 may include determining whether the at leastone shopper is eligible for frictionless checkout based on the detectedat least one product interaction event. As discussed above, processingdevice 202 may determine whether shopper (e.g., 1202, 1204, etc.) iseligible for frictionless checkout based on a detected productinteraction event. As also discussed in detail above, processing devicemay employ one or more of many different criteria to determine whether ashopper (e.g., 1202, 1204, etc.) is ineligible for frictionlesscheckout. When processing device 202 determines that a shopper (e.g.,1202, 1204, etc.) is eligible for frictionless checkout (Step 1408:Yes), process 1400 may return to step 1402. When processing device 202determines, however, that a shopper (e.g., 1202, 1204, etc.) is noteligible for frictionless checkout (Step 1408: No), process 1400 mayproceed to step 1410. Some other examples are described below, forexample in relation to FIGS. 24-26 .

In step 1410, process 1400 may include causing delivery of an indicatorthat the at least one shopper is ineligible for frictionless checkout.For example, processing device 202 may generate an indicator, indicatingwhether a shopper is eligible or ineligible for frictionless checkout.The indicator may be in the form of a numerical value, a textualmessage, and/or a symbol or image. Processing device 202 may also beconfigured to adjust a color, or font, and/or other displaycharacteristics of the indicator. Processing device 202 may beconfigured to transmit the indicator to a device associated with theretailer and/or with the shopper. For example, processing device 202 maybe configured to transmit the indicator to a wearable device (e.g., asmartwatch, a smart glass, etc.) associated with the shopper. Theindicator received from processing device 202 may be displayed on adisplay associated with the wearable device. It is contemplated thatadditionally or alternatively, processing device 202 may transmit theindicator to one or more mobile devices (e.g., a smart form, a tabletcomputer, a laptop computer, etc.) associated with the shopper. FIG. 13Aillustrates an exemplary smartphone 1310 having a display 1320. It isalso contemplated that in some embodiments a shopping cart (e.g., 1230)being used by a shopper (e.g., 1202) may be equipped with an indicatoror display device, and display device on the shopping cart may beconfigured to display an indicator, indicating whether the shopper iseligible or ineligible for frictionless checkout.

Traditionally, customers of brick-and-mortar retail stores collect theproducts they wish to purchase, and then wait in a shopping line to payat a checkout counter. The checkout counter may be a self-checkoutpoint-of-sale system or serviced by a store associate of the store whoscans all of the items before the items are paid for by the customers.Nowadays, retail stores seek ways to provide a frictionless checkoutexperience to improve customer service. Frictionless shopping cases andspeeds up the buying process, because the products that customerscollect are automatically identified and assigned to a virtual shoppingcart associated with the appropriate customer. This way, customers mayskip spending time in a shopping line and simply leave the retail storewith the products they collected.

Enabling frictionless checkout may look easy, but actually it mayrequire an exceptionally complex process that takes into considerationdifferent scenarios. For example, depending on detected conditions orother circumstances, a particular retail shelf may be eligible forfrictionless checkout or ineligible for frictionless checkout. Thepresent system provides a visual indicator that may be automaticallyupdated to indicate a current status of a retail shelf or portion of aretail shelf. The visual indicator may inform shoppers whether items ona shelf or a portion of a shelf are eligible for frictionless checkout.With this information, customers may choose to avoid products noteligible for frictionless checkout or may choose such products withadvance knowledge that traditional checkout will be required.Additionally, this information may enable store associates to attend toshelves not eligible for frictionless checkout and to rectify conditionspreventing frictionless checkout eligibility.

As noted generally above, a retail environment may provide africtionless checkout experience. As used herein, a frictionlesscheckout refers to any checkout process for a retail environment with atleast one aspect intended to expedite, simplify, or otherwise improve anexperience for customers. In some embodiments, a frictionless checkoutmay reduce or eliminate the need to take inventory of products beingpurchased by the customer at checkout. For example, this may includetracking the selection of products made by the shopper so that they arealready identified at the time of checkout. The tracking of products mayoccur through the implementation of sensors used to track movement ofthe shopper and/or products within the retail environment, as describedthroughout the present disclosure. Additionally or alternatively, africtionless checkout may include an expedited or simplified paymentprocedure. For example, if a retail store has access to paymentinformation associated with a shopper, the payment information may beused automatically or upon selection and/or confirmation of the paymentinformation by the user. In some embodiments, a frictionless checkoutmay involve some interaction between the user and a store associate orcheckout device or terminal. In other embodiments, the frictionlesscheckout may not involve any interaction. For example, the shopper maywalk out of the store with the selected products and a paymenttransaction may occur automatically. While the term “frictionless” isused for purposes of simplicity, it is to be understood that thisencompasses semi frictionless checkouts as well. Accordingly, varioustypes of checkout experiences may be considered “frictionless,” and thepresent disclosure is not limited to any particular form or degree offrictionless checkout.

FIGS. 15A-15D illustrate example visual indicators 1500A-1500D(collectively referred to as visual indicators 1500) indicative of thefrictionless checkout statuses of portions of retail shelves accordingto disclosed embodiments. FIGS. 15A and 15B illustrate examples ofhardware solutions physically installed in retail store 105.Specifically, FIG. 15A illustrates how visual indicators may bedisplayed via light sources associated with different portions of aretail shelf, and FIG. 15B illustrates how visual indicators may bedisplayed via display units associated with different portions of aretail shelf. FIGS. 15C and 15D illustrate examples of softwaresolutions that use a mobile communication device of an individual inretail store 105. Specifically, FIG. 15C illustrates how visualindicators may be displayed via a mobile device associated with anindividual in retail store 105, and FIG. 15D illustrates how visualindicators may be displayed via an Augmented Reality (AR) systemassociated with an individual in the retail store.

With reference to FIG. 15A and consistent with the present disclosure,visual indicator 1500A is displayed via one or more light sourcesassociated with at least a portion of a retail shelf (e.g., store shelf510). The one or more light sources may be part of the shelf or part ofa device attachable to the shelf. In the illustrated example, visualindicator 1500A-1 indicates that the products 1502 are ineligible forfrictionless checkout, and visual indicators 1500A-2 indicate that therest of the products are eligible for frictionless checkout. In oneembodiment, visual indicator 1500 may include a color associated withthe one or more light sources. For example, a green light may indicatethat products associated with the at least a portion of a retail shelfmay be eligible for frictionless checkout; and a red light may indicatethat products associated with the at least a portion of a retail shelfmay be ineligible for frictionless checkout.

With reference to FIG. 15B and consistent with the present disclosure,visual indicators 1500B are displayed via display units 1504 associatedwith different portions of a retail shelf. The display units 1504 may bepart of the shelf or may be attachable to the shelf. In the illustratedexample, visual indicator 1500B-1 indicates that the products 1502 areineligible for frictionless checkout, and visual indicators 1500B-2indicate that the rest of the products are eligible for frictionlesscheckout. In one embodiment, visual indicator 1500 may include textshown on the display. For example, the text “frictionless” may indicatethat products associated with the at least a portion of a retail shelfmay be eligible for frictionless checkout; and the text“non-frictionless” may indicate that products associated with the atleast a portion of a retail shelf may be ineligible for frictionlesscheckout.

With reference to FIG. 15C and consistent with the present disclosure,visual indicators 1500C are displayed via a mobile device 1506associated with an individual in the retail store. Mobile device 1506may be associated with a shopper in the retail store or a storeassociate of the retail store. Consistent with the present disclosure,mobile device 1506 may include a handheld device (e.g., a smartphone, atablet, a mobile station, a personal digital assistant, a laptop, andmore) or a wearable device (e.g., smart glasses, a smartwatch, a clip-oncamera). In the illustrated example, each of the visual indicatorsdisplayed by mobile device 1506 is tied to a specific product, and thereare two types of indicators: 1500C-1, indicating that an associatedproduct is eligible for frictionless checkout; and 1500C-2, indicatingthat an associated product is ineligible for frictionless checkout.

With reference to FIG. 15D and consistent with the present disclosure,visual indicators 1500D are displayed via an extended reality (XR)system 1508 associated an individual in the retail store. XR system 1508may be associated with a shopper in the retail store or a storeassociate of the retail store. Consistent with the present disclosure,XR system 1508 may include a Virtual Reality (VR) device, an AugmentedReality (AR) device, a Mixed Reality (MR) device, smart glasses, mobiledevices, mobile phones, smartphones, and so forth. Some non-limitingexamples of XR system 1508 may include Nreal Light, Magic Leap One,Varjo, Quest 1, Quest 2, Vive, and so forth. In the illustrated example,each of the visual indicators displayed by XR system 1508 is tied to aspecific product, and there is only one type of indicators 1500D thatindicates that an associated product is ineligible for frictionlesscheckout. In this case, the absence of the automatically generatedvisual indicator 1500 indicates that the other products are eligible forfrictionless checkout.

FIG. 16 illustrates an exemplary embodiment of a memory device 1600containing software modules consistent with the present disclosure. Inparticular, as shown, memory device 1600 may include a sensorscommunication module 1602, a captured data analysis module 1604, aproduct data determination module 1606, a frictionless checkouteligibility status determination module 1608, a visual indicator displaymodule 1610, a database access module 1612, and a database 1614. Modules1602, 1604, 1606, 1608, 1610, and 1612 may contain software instructionsfor execution by at least one processor (e.g., processing device 202)associated with system 100. Sensors communication module 1602, captureddata analysis module 1604, product data determination module 1606,frictionless checkout eligibility status determination module 1608,visual indicator display module 1610, database access module 1612, anddatabase 1614 may cooperate to perform various operations. For example,sensors communication module 1602 may receive an data from one or moresensors in retail store 105, captured data analysis module 1604 may usethe received data to determine information about a displayed inventoryof products on shelves of retail store 105, product data determinationmodule 1606 may obtain product data about the type of products on theretail shelves, frictionless checkout eligibility status determinationmodule 1608 may use information about the displayed inventory of aplurality of products and/or the product data to determine africtionless checkout eligibility status associated with at least aportion of a retail shelf, and visual indicator display module 1610 maycause a display of a visual indicator indicative of the frictionlesscheckout eligibility status.

According to disclosed embodiments, memory device 1600 may be part ofsystem 100, for example, memory device 226. Alternatively, memory device1600 may be stored in an external database or an external storagecommunicatively coupled with server 135, such as one or more databasesor memories accessible over communication network 150. Further, in otherembodiments, the components of memory device 1600 may be distributed inmore than one server and more than one memory device.

In some embodiments, sensors communication module 1602 may receiveinformation from sensors 1601, located in retail store 105. In oneexample, sensors communication module 1602 may receive image data (e.g.,images or video) captured by a plurality of image sensors fixedlymounted in retail store 105 or derived from images captured by aplurality of image sensors fixedly mounted in retail store 105. Inanother example, sensors communication module 1602 may receive imagedata (e.g., images or data derived from images) from robotic capturingdevices configured to navigate autonomously within retail store 105 andto capture images of multiple types of products. In yet another example,sensors communication module 1602 may receive data from one or moreshelf sensors disposed on a surface of the at least a portion of theretail shelf configured to hold one or more products placed on the atleast a portion of the retail shelf. The one or more shelf sensors mayinclude pressure sensitive pads, touch-sensitive sensors, lightdetectors, weight sensors, light sensors, resistive sensors, ultrasonicsensors, and more.

In some embodiments, captured data analysis module 1604 may process theinformation collected by sensors communication module 1602 to determineinformation about the displayed inventory of products on the shelves ofretail store 105. In one embodiment, captured data analysis module 1604may determine the information about the displayed inventory of productson shelves of retail store 105 solely based on image data, for example,image data received from a plurality of image sensors fixedly mounted inretail store 105 (e.g., as illustrated in FIG. 4A). In anotherembodiment, captured data analysis module 1604 may determine theinformation about the displayed inventory of products on the shelves ofretail store 105 using a combination of image data and data from one ormore retail store sensors configured to measure properties of productsplaced on a store shelf (e.g., as illustrated in FIG. 8A). For example,captured data analysis module 1604 may analyze the data received fromdetection elements attached to store shelves, alone or in combinationwith images captured in retail store 105 (e.g., using robotic capturingdevices).

In some embodiments, product data determination module 1606 maydetermine product data about the products placed on the shelves ofretail store 105. The product data may be determined using informationcollected from one or more of entities in the supply chain and otherdata sources, for example, Enterprise Resource Planning (ERP), WarehouseManagement Software (WMS), and Supply Chain Management (SCM)applications. In addition, product data determination module 1606 maydetermine the product data using analytics of data associated with pastdelivery and sales of the products. Consistent with the presentdisclosure, the product data may be used to determine time periods ofeligibility and time periods of ineligibility for different types ofproducts.

In one embodiment, the product data may be determined based on demanddata for products placed on shelves of retail store 105. The demand datamay be obtained using forecasting algorithms, including statisticalalgorithms such as Fourier and multiple linear regression algorithms.The forecasting algorithms may use a variety of factors relating todifferent perishable products, and various types of demand history data(e.g., shipments data, point-of-sale data, customer order data, returndata, marketing data, and more). Generally, demand history data may bebroken into two types: base and non-base. Base history data includespredictable demand data that may be repeatable. Conversely, non-basehistory data is that part of demand that is due to special events, suchas promotions or extreme market circumstances. In another embodiment,the product data may be determined based on scheduling data receivedfrom one or more of entities in the supply chain. For example, thescheduling data may be obtained from online services (e.g., from aserver that store data on shipments orders), from supplier 115associated with the products (e.g., from a farmer that produced theproducts), from a market research entity 110 (e.g., statistics aboutdemand for certain products), from a shipment company that delivers theproducts (e.g., from an IOT sensor in a cargo ship), or from adistribution company that delivers the products (e.g., from an agent whosupplies the products to retail stores).

Frictionless checkout eligibility status determination module 1608 maydetermine the frictionless checkout eligibility status associated withat least a portion of a retail shelf and/or the frictionless checkouteligibility status associated with specific products placed on theretail shelf. In a first embodiment, frictionless checkout eligibilitystatus determination module 1608 may determine the frictionless checkouteligibility status using solely information from sensors communicationmodule 1602. In a second embodiment, frictionless checkout eligibilitystatus determination module 1608 may determine the frictionless checkouteligibility status using information from sensors communication module1602 and information from product data determination module 1606.Consistent with the present disclosure, frictionless checkouteligibility status determination module 1608 may use artificial neuralnetworks, convolutional neural networks, machine learning models, imageregression models, and other processing techniques to determine thefrictionless checkout eligibility status. For example, captured dataanalysis module 1604 may calculate a convolution of at least part of theimage data. In response to a first value of the calculated convolution,frictionless checkout eligibility status determination module 1608 maydetermine a first frictionless checkout eligibility status associatedwith the at least a portion of the retail shelf; and in response to asecond value of the calculated convolution, frictionless checkouteligibility status determination module 1608 may determine a secondfrictionless checkout eligibility status associated with the at least aportion of the retail shelf, the second frictionless checkouteligibility status may be differ from the first frictionless checkouteligibility status.

Consistent with an embodiment, frictionless checkout eligibility statusdetermination module 1608 may determine the frictionless checkouteligibility status based on an arrangement of products placed on the atleast a portion of the retail shelf as reflected in the output receivedfrom sensors 1601. The arrangement of products placed on the at least aportion of the retail shelf may include the number of products, theirplacement pattern, etc. In one example, the at least a portion of theretail shelf may correspond to a first product type, and in response toa product of a second product type being placed on the at least aportion of the retail shelf, frictionless checkout eligibility statusdetermination module 1608 may determine that the frictionless checkouteligibility status for products associated with the at least a portionof the retail shelf is ineligible.

Frictionless checkout eligibility status determination module 1608 maydetermine the frictionless checkout eligibility status further based onproduct data associated with the type of products placed on the at leasta portion of the retail shelf. For example, some products may be onsale, and to increase sales, frictionless checkout eligibility statusdetermination module 1608 may determine that they are eligible forfrictionless checkout even when certain conditions do not exist. In oneembodiment, a threshold determined based on the product data may be usedto determine the frictionless checkout eligibility status associatedwith the at least a portion of the retail shelf. For example, thethreshold may be determined based on the type of products, based on aphysical dimension of products of the product type, based on a priceassociated with the product type, based on a risk for thefts associatedwith the product type, and so forth.

In some embodiments, visual indicator display module 1610 may cause adisplay of an automatically generated visual indicator based on theoutput of frictionless checkout eligibility status determination module1608. The visual indicator is indicative of the frictionless checkouteligibility status associated with the at least a portion of the retailshelf. Examples of visual indicators generated by visual indicatordisplay module 1610 are illustrated in FIGS. 15A-D and described indetail above. In one embodiment, the display of the automaticallygenerated visual indicator indicates that the at least a portion of theretail shelf is not eligible for frictionless checkout, and an absenceof the automatically generated visual indicator indicates that the atleast a portion of the retail shelf is eligible for frictionlesscheckout.

In some embodiments, database access module 1612 may cooperate withdatabase 1614 to retrieve stored product data. The retrieved productdata may include, for example, sales data, theft data (e.g., alikelihood that a certain product may be subject to shoplifting), aschedule of arrivals of additional products, inventory records, checkoutdata, calendar data, historical product turnover data, and more. Asdescribed above, frictionless checkout eligibility status determinationmodule 1608 may use the product data stored in database 1614 todetermine the frictionless checkout eligibility status. Database 1614may include separate databases, including, for example, a vectordatabase, raster database, tile database, viewport database, and/or auser input database, configured to store data. The data stored indatabase 1614 may be received from modules 1602-1612, server 135, fromany communication device associated with retail stores 105, marketresearch entity 110, suppliers 115, users 120, and more. Moreover, thedata stored in database 1614 may be provided as input using data entry,data transfer, or data uploading.

Modules 1602-1612 may be implemented in software, hardware, firmware, amix of any of those, or the like. For example, if the modules areimplemented in software, the modules may be stored in a server (e.g.,server 135) or distributed over a plurality of servers. In someembodiments, any one or more of modules 1602-1612 and data associatedwith database 1614 may be stored in database 140 and/or located onserver 135, which may include one or more processing devices. Processingdevices of server 135 may be configured to execute the instructions ofmodules 1602-1612. In some embodiments, aspects of modules 1602-1612 mayinclude software, hardware, or firmware instructions (or a combinationthereof) executable by one or more processors, alone, or in variouscombinations with each other. For example, modules 1602-1612 may beconfigured to interact with each other and/or other modules of server135 to perform functions consistent with disclosed embodiments.

FIG. 17A depicts a flowchart of an example process 1700 executed by aprocessing device of system 100 (e.g., processing device 202) forupdating visual indicator indicating the frictionless checkouteligibility status. For purposes of illustration, in the followingdescription, reference is made to certain components of system 100. Itwill be appreciated, however, that other implementations are possibleand that other components may be used to implement example process 1700.It will also be readily appreciated that the example process 1700 may bealtered to modify the order of steps, delete steps, or further includeadditional steps.

Process 1700 begins when the processing device determines a frictionlesscheckout eligibility status (decision block 1702). The frictionlesscheckout eligibility status may be determined for certain products, forcertain product types, for a portion of a shelf, for a whole shelf, orfor an area in retail store 105. As mentioned above, the frictionlesscheckout eligibility status may be determined based on sensor data,product data, or a combination thereof. When the frictionless checkouteligibility status is determined to be appropriate, the processingdevice may cause a display of visual indicator 1500 (block 1704).Thereafter, the processing device may determine if a change of thefrictionless checkout eligibility status is needed based on an obtainedoverride signal (decision block 1706). The override signal may bedetermined based on the product data or received from a store associateof retail store 105. For example, the manager of the retail store maywish to maintain a non-frictionless status for some products regardlessof sensor outputs. In case no override signal was received, theprocessing device may determine if a change of the frictionless checkouteligibility status is needed based on detection of a status change event(decision block 1708). If a status change event was not detected, visualindicator 1500 of block 1704 may be maintained.

When either an override signal is obtained or a status change event isdetected, the processing device may cause a display of visual indicator1500 indicative of a non-frictionless status (block 1710). Thereafter,the processing device may determine if a change of the frictionlesscheckout eligibility status is needed, based on an obtained overridesignal (decision block 1712) or detection of a status change event(decision block 1714). Consistent with the present disclosure, a statuschange event may be detected based on output from the one or more retailstore sensors (e.g., sensors 1601). For example, the status change eventmay include an identification of at least two shoppers standing in avicinity of at least a portion of the retail shelf, or an identificationof a shopper that placed a product on the at least a portion of theretail shelf, or a determination of an orientation of a shopper opposingthe portion of the retail shelf, or a determination that the output fromthe one or more retail store sensors is not sufficient for determiningthat the at least a portion of the retail shelf is eligible forfrictionless shopping.

The following scenario is an example of how process 1700 may beimplemented in the retail store. Initially, a first shopper is standingnext to a retail shelf holding canned fish. The retail shelf includes adevice with at least one light source that displays visual indicator1500A-2 indicating that the retail shelf is eligible for frictionlessshopping. While the first shopper is considering which fish to buy, asecond shopper comes and stands next to the first shopper. The coming ofthe second shopper may be considered as a status change event, becausethe system may not be able to determine which shopper picked a can ofsardines and which shopper picked a can of mackerel, e.g., at acertainty level greater than a threshold. In accordance with decisionblock 1708, the processing device may cause the device to display visualindicator 1500A-1 to inform the first and second shoppers that pickingcanned fish at this time would require them to complete a traditionalcheckout. When one of the shoppers steps away from the retail shelf, theprocessing device causes the device to display again visual indicator1500A-2, because the leaving of the shopper may also be considered as astatus change event, as shown in step 1714.

FIG. 17B is a flowchart of an example process 1750 for providing avisual indicator indicative of a frictionless checkout status of atleast a portion of a retail shelf executed by a processing device ofsystem 100, according to embodiments of the present disclosure. Theprocessing device of system 100 may include at least one processorwithin image processing unit (e.g., server 135) or any processorassociated with retail store 105. For purposes of illustration, in thefollowing description, reference is made to certain components of system100. It will be appreciated, however, that other implementations arepossible and that any combination of components or devices may beutilized to implement the exemplary method. It will also be readilyappreciated that the illustrated method may be altered to modify theorder of steps, delete steps, or further include additional steps, suchas steps directed to optional embodiments.

In some embodiments, the processing device of system 100 may “receive anoutput from one or more retail store sensors. As discussed earlier,various types of sensors may be used to monitor inventory of products inretail store 105. By way of example only, at step 1752 in FIG. 17B, aprocessing device (e.g., processing device 202) may receive an outputfrom one or more retail store sensors. In an embodiment, the one or moreretail store sensors are disposed on a surface of the at least a portionof the retail shelf configured to hold one or more products placed onthe at least a portion of the retail shelf. For example, the one or moreretail store sensors may include at least one shelf sensor, such as aweight-sensitive sensor, a touch-sensitive sensor, a pressure-sensitivesensor, a light-sensitive sensor, or any combination thereof. In anotherembodiment, the one or more retail store sensors may include at leastone image sensor configured to capture one or more images of the atleast a portion of the retail shelf, and the output includes image datacaptured using the at least one image sensor. In yet another embodiment,the one or more retail store sensors includes at least one image sensorand at least one shelf sensor.

In some embodiments, the processing device of system 100 may determine africtionless checkout eligibility status associated with the at least aportion of the retail shelf. The term “frictionless checkout eligibilitystatus associated with the at least a portion of the retail shelf” isused to denote a reference value, a level, a point, or a range ofvalues, for determining if a shopper that picked a product from the atleast a portion of the retail shelf is entitled to frictionlesscheckout. In one example, the frictionless checkout eligibility statusmay be either of the terms “frictionless” or “non-frictionless,” whichare used to describe whether the at least a portion of the retail shelfincludes one or more items eligible for frictionless checkout.Alternatively, the frictionless checkout eligibility status may includea value representing a frictionless checkout eligibility score, and thesystem may determine whether a shopper is entitled to frictionlesscheckout based on the overall scores of the product he or she picked. Asdiscussed above, the determination of the frictionless checkouteligibility status may be based on the output from the one or moreretail store sensors. Consistent with the present disclosure, adetermination that a portion of the retail shelf is entitled to africtionless checkout eligibility status occurs when all the items(e.g., products) placed on that portion of the retail shelf are entitledto a frictionless checkout eligibility status. Whereas a determinationthat a portion of the retail shelf is not entitled to a frictionlesscheckout eligibility status occurs when at least one the items placed onthat portion of the retail shelf is not entitled to a frictionlesscheckout eligibility status. By way of example only, at step 1754 inFIG. 17B, the processing device may determine a frictionless checkouteligibility status associated with the at least a portion of the retailshelf.

In related embodiments, the processing device may calculate aconvolution of at least part of the image data captured by the one ormore retail store sensors. Thereafter, in response to a first value ofthe calculated convolution, the processing device may determine a firstfrictionless checkout eligibility status associated with the at least aportion of the retail shelf. In response to a second value of thecalculated convolution, the processing device may determine a secondfrictionless checkout eligibility status associated with the at least aportion of the retail shelf. The second frictionless checkouteligibility status differs from the first frictionless checkouteligibility status. For example, the first frictionless checkouteligibility status may be “frictionless,” and the second frictionlesscheckout eligibility status may be “non-frictionless.” In additionalembodiments, the processing device may determine the frictionlesscheckout eligibility status based on an arrangement of products placedon the at least a portion of the retail shelf. The arrangement ofproducts detected may be based on the output received from the one ormore sensors. In one example, the at least a portion of the retail shelfmay correspond to a first product type, and in response to a product ofa second product type being placed on the at least a portion of theretail shelf, the processing device may determine that the at least aportion of the retail shelf is ineligible to frictionless checkout.

In some embodiments, the processing device of system 100 may cause adisplay of an automatically generated visual indicator indicating thefrictionless checkout eligibility status associated with the at least aportion of the retail shelf. In this disclosure, the term “associatedwith the at least a portion of the retail shelf” means that the visualindicator is displayed in proximity to the portion of the retail shelf.Consistent with the present disclosure, the processing device may causea display of a first visual indicator indicative of a first frictionlesscheckout eligibility status associated with a first portion of a certainretail shelf and may cause a display of a second visual indicatorindicative of a second frictionless checkout eligibility statusassociated with a second portion of the certain retail shelf.Alternatively, the processing device may cause a display of a visualindicator indicative of the first frictionless checkout eligibilitystatus and an absence of the automatically generated visual indicatormay indicates the second frictionless checkout eligibility status. Byway of example only, at step 1756 in FIG. 17B, the processing device maycause a display of an automatically generated visual indicatorindicating the frictionless checkout eligibility status associated withthe at least a portion of the retail shelf. As illustrated in FIG. 15A,the visual indicator may be displayed via at least one light sourceassociated with the at least a portion of the retail shelf. For example,the at least one light source may be part of the shelf or part of adevice attachable to the shelf. Moreover, the visual indicator mayinclude a color associated with the light source, e.g., green forfrictionless and red for non-frictionless. As illustrated in FIG. 15B,the visual indicator may be displayed via a display unit associated withthe at least a portion of the retail shelf, wherein the visual indicatorincludes text shown on the display. As illustrated in FIG. 15C, thevisual indicator may be displayed via one or more mobile devicesassociated with a shopper in the retail store or associated with a storeassociate of the retail store. As illustrated in FIG. 15D, the visualindicator may be displayed via an augmented reality (AR) systemassociated with a shopper in the retail store or with a store associateof the retail store.

In some embodiments, the processing device of system 100 may determine achange in the frictionless checkout eligibility status associated withthe at least a portion of the retail shelf based on a detected statuschange event indicated by the output from the one or more retail storesensors. In this disclosure, the term “status change event” refers toany combination of conditions that may potentially decrease a certaintylevel the system has in identifying a shopper-product interaction. Asshown in FIG. 17A, upon detecting a status change event, the processingdevice may update the visual indicator to reflect the change in thefrictionless checkout eligibility status. In one embodiment, the statuschange event may include an identification of at least two shoppersstanding in a vicinity of the at least a portion of the retail shelf. Inanother embodiment, the status change event may include anidentification of a shopper that placed a product on the at least aportion of the retail shelf. For example, the shopper may place aproduct on the shelf different from the other products on the shelf. Inanother embodiment, the status change event may include a determinationof an orientation of a shopper opposing the at least a portion of theretail shelf. In another embodiment, the status change event may includea determination that the output from the one or more retail storesensors is not sufficient for determining whether the at least a portionof the retail shelf is eligible for frictionless shopping. For example,an image sensor may be blocked by some kind of an object, or one of theshelf sensors may be broken.

In some embodiments, the processing device of system 100 may obtaininput related to a type of products placed on the at least a portion ofthe retail shelf; and determining the frictionless checkout eligibilitystatus of the at least a portion of the retail shelf based on theobtained input. The input may be related to a type of products and maydefine time periods of eligibility and time periods of ineligibility. Insome cases, the input may include the product data, as described abovewith reference to FIG. 16 . In one example, the input related to a typeof products may include an indication that the products associated withthe portion of the shelf are on sale, so to increase sales, theprocessing device may determine it should be eligible for frictionlesseven when certain conditions do not exist. Specifically, a threshold maybe used to determine the frictionless checkout eligibility statusassociated with the at least a portion of the retail shelf In oneexample, the threshold may be selected based on the type of products,e.g., based on a physical dimension of products of the product type,based on a price associated with the product type, based on a risk forthefts associated with the product type, and so forth. In a relatedembodiment, the processing device may obtain a status override signal,and determine the frictionless checkout eligibility status of the atleast a portion of the retail shelf based on the status override signal.For example, for some products, it may be desirable to maintain africtionless eligibility status regardless of sensor outputs.

As described throughout the present disclosure, a retail environment mayallow a frictionless (or semi-frictionless) shopping experience forusers or customers. For example, this may include expediting or eveneliminating a checkout process a customer must complete to make apurchase. In some embodiments, this may include automatically trackingproducts a customer has selected (e.g., by placing items in a cart orbasket, etc.) and completing a transaction associated with the productswithout requiring the customer to present the items to a cashier, scaneach item individually, present a payment method, or the like.Additional details regarding frictionless shopping experiences areprovided throughout the present disclosure.

In order to provide a frictionless shopping experience for shoppers, itmay be beneficial for a retailer to track or determine a shopper'seligibility for semi-frictionless shopping. Various conditions or eventsmay cause a shopper to lose eligibility for frictionless checkout. Forexample, a shopper may be near one or more other shoppers when a certainproduct is selected, which may lead to ambiguity regarding which of theshoppers removed the product from the shelf. Such ambiguity may causethe shopper to lose eligibility for frictionless checkout, as the exactinventory of the shopper's cart, for example, may be unknown. In someembodiments, characteristics or information about a shopper may lead toineligibility, such as a lack of frictionless payment informationassociated with the user being available.

In situations where a shopper's eligibility for frictionless checkouthas been lost or has not yet been established, it may be beneficial fora retailer to restore eligibility for shoppers. In some embodiments,this restoration may occur automatically and without action by theshopper. The disclosed embodiments allow for various actions to be takento automatically restore a shopper's eligibility for frictionlesscheckout. For example, the shopper may be requested to scan an itempreviously placed in a cart (e.g., using a barcode scanner) or positionthe contents of a cart or basket before a camera. A store associate maybe dispatched to rectify a detected ambiguity, or an action taken by oneshopper may rectify an ambiguity associated with another shopper (e.g.,if two shoppers are involved in an ambiguous product selection event,the ambiguity may be resolved during checkout of one of the shopperswhere the items selected by that shopper can be confirmed). Any of theseactions or events may result in an eligibility for frictionless checkoutto be returned to a shopper.

As noted generally above, a retail environment may provide africtionless checkout experience. As used herein, a frictionlesscheckout refers to any checkout process for a retail environment with atleast one aspect intended to expedite, simplify, or otherwise improve anexperience for customers. In some embodiments, a frictionless checkoutmay reduce or eliminate the need to take inventory of products beingpurchased by the customer at checkout. For example, this may includetracking the selection of products made by the shopper so that they arealready identified at the time of checkout. The tracking of products mayoccur through the implementation of sensors used to track movement ofthe shopper and/or products within the retail environment, as describedthroughout the present disclosure. Additionally or alternatively, africtionless checkout may include an expedited or simplified paymentprocedure. For example, if a retail store has access to paymentinformation associated with a shopper, the payment information may beused automatically or upon selection and/or confirmation of the paymentinformation by the user. In some embodiments, a frictionless checkoutmay involve some interaction between the user and a store associate orcheckout device or terminal. In other embodiments, the frictionlesscheckout may not involve any interaction. For example, the shopper maywalk out of the store with the selected products and a paymenttransaction may occur automatically. While the term “frictionless” isused for purposes of simplicity, it is to be understood that thisencompasses semi-frictionless checkouts as well. Accordingly, varioustypes of checkout experiences may be considered “frictionless,” and thepresent disclosure is not limited to any particular form or degree offrictionless checkout.

To participate in a frictionless checkout process, a shopper may berequired to be designated as having frictionless checkout eligibility.Various ineligibility conditions may arise that cause a shopper to bedesignated as not eligible for frictionless checkout. An ineligibilitycondition may include any condition in which the disclosed system hasinsufficient information for completing a frictionless checkout. In someembodiments, an ineligibility condition may include some degree ofuncertainty relative to a product selection by a shopper. For example,an ambiguous product interaction event may be detected, which mayinclude any event resulting in uncertainty about whether one or moreproducts have been selected by a particular shopper. An ambiguousproduct interaction event may include an event in which one or moresensors, such as capturing device 125 described above, is unable tofully or accurately capture enough information to determine whether aproduct has been selected by a shopper. For example, a shopper'sinteraction with a product may be fully or partially obscured from viewof the sensor, leading to uncertainty as to whether the product wasselected.

FIG. 18 illustrates an example ambiguous product interaction event 1800that may be detected, consistent with the disclosed embodiments. Asshown in FIG. 18 , a shopper 1820 may interact with a product 1810 in aretail environment. For example, this may include, looking at product1810, stopping in front of product 1810, picking up product 1810 from ashelf 1802, returning product 1810 to shelf 1802, placing product 1810in a shopping cart 1822 associated with shopper 1820, or various otherforms of interaction. In some embodiments, the interaction betweenshopper 1820 and product 1810 may at least partially be detected by asensor. In some embodiments, the sensor may include a camera 1840, asshown in FIG. 18 . Camera 1840 may include any device capable ofcapturing one or more images from within a retail environment. In someembodiments, camera 1840 may correspond to image capture device 125(including devices 125A, 125B, 125C, 125D, 125E, 125F, or 125G) asdescribed above. Accordingly, any embodiments or features described inreference to image capture device 125 may equally apply to camera 1840.Camera 1840 (and in some cases, additional image capture devices) may beused to identify shoppers 1820 and 1830 in the retail environment, aswell as product 1810, as described in further detail above.

In some embodiments, ambiguous product interaction event 1800 may occurdue to a view of camera 1840 being at least partially blocked. Forexample, shopper 1820 may be positioned such that the interaction withproduct 1810 by shopper 1820 is blocked by the body of shopper 1830.Accordingly, it may be unclear whether the product was selected orreturned to the shelf. Or, if a product was selected, it may not beclear which product was removed from the shelf. In some embodiments, anindividual or object may block the view of camera 1840. For example,another shopper 1830 may be positioned such that the interaction withproduct 1810 is blocked, as shown in FIG. 18 . Various other types ofevents related to camera 1840 may cause uncertainty as to theinteraction with product 1810. For example, this may include a temporaryloss of connection to camera 1840, a loss of power to camera 1840, amalfunction, improper positioning of camera 1840, dirt or debris on alens of camera 1840, a failure to recognize product 1810 using imagescaptured by camera 1840 (e.g., due to movement of product 1810, blurryor low-resolution images, etc.), or any other even that may lead touncertainty in recognizing whether or not product 1810 was selected by aparticular shopper. Further, while camera 1840 is used by way ofexample, ambiguous product interaction events may arise due touncertainty associated with other types of sensors, including pressuresensors, weight sensors, light sensors, resistive sensors, capacitivesensors, inductive sensors, vacuum pressure sensors, high pressuresensors, conductive pressure sensors, infrared sensors, photo-resistorsensors, photo-transistor sensors, photo-diodes sensors, ultrasonicsensors, or the like.

Based on the failure to fully capture the interaction with product 1810,various forms of uncertainty may arise. For example, it may be unclearwhether product 1810 was selected by shopper 1820 (e.g., placed intoshopping cart 1822) or was returned to shelf 1802. It may also beunclear whether product 1810 was selected or whether a different productfrom shelf 1802 was selected. In other words, an ambiguous productinteraction event may involve a shopper adding or removing anunrecognized object from cart or basket. In some embodiments, a retailstore system may correctly identify which product or product type wasselected, but it may be unclear which shopper has selected the product.In the example shown in FIG. 18 , it may not be clear whether product1810 is placed in shopping cart 1822 (and thus associated with shopper1820) or placed in shopping cart 1832 (and thus associated with shopper1830). In another example, two shoppers may pick products from a shelfat approximately the same time, for example each picking one product,and it may be unclear which product was picked by which shopper. In yetanother example, one or more shoppers may interact with a portion of aretail shelf that do not support frictionless checkout at the time ofthe interaction (for example, as described above), and the ambiguity maybe due to the interaction with the portion of the retail shelf when itdoes not support frictionless checkout. It is to be understood that theambiguous product interaction event illustrated in FIG. 18 is providedby way of example, and various other scenarios may lead to uncertaintyleading to an ineligibility status of a shopper for frictionlesscheckout.

Consistent with the disclosed embodiments, ineligibility conditions mayarise in various other types of scenarios. In some embodiments, anineligibility status may include a lack of an available form ofautomatic payment for a shopper. For example, the shopper may not haveprovided payment information, may not have selected or approvedpreviously stored payment information for frictionless chcckout, may beassociated with expired payment information, or various other issuesthat may make automatic payment information available for frictionlesscheckout. In some embodiments, the automatic payment information for ashopper may be stored in a shopper profile, such as shopper profile 1910described in further detail below with respect to FIG. 19A.Ineligibility conditions may be identified based on other factors,including a shopper being unrecognized by the disclosed system, ashopper leaving view of one or more cameras or other sensors, a productor item being disqualified for frictionless checkout (e.g., an itemrequiring weighing, a sale item, a seasonal or promotional item, anoversized or heavy item, etc.), or various other conditions that mayinhibit or interfere with frictionless checkout.

Based on a detected ineligibility condition, such as an ambiguousproduct interaction event, one or more shoppers may be designated asineligible for frictionless checkout. In some embodiments, this mayinclude updating a frictionless checkout status for a shopper in ashopper profile. FIG. 19A illustrates an example shopper profile 1910that may be associated with a shopper, such a shopper 1820. Shopperprofile 1910 may be associated with a specific store or retailerlocation, a specific chain or brand of retailer, or a specific family ofretailers, or may be a global profile associated not associated with anyparticular retailer. As shown in FIG. 19A, shopper profile 1910 mayinclude identifying information for a shopper, such as a name of theshopper, a customer ID or other identifier, a phone number, an address,demographic information, a retailer or merchant associated with theshopper, or any other information that may be associated with a shopper.As noted above, shopper profile 1910 may include payment information1912. For example, this may include credit card information or otherfinancial account information used complete a transaction. In someembodiments, payment information 1912 may further include a rewards orfrequent shopper membership information, coupons or discounts associatedwith a shopper, enrollment in frictionless checkout, or any otherinformation associated with payment. In some embodiments, multiplepayment methods may be stored in shopper profile 1910. Accordingly, ashopper may select one or more payment methods to be automatically usedfor frictionless checkout.

In some embodiments, shopper profile 1910 may include a frictionlesscheckout status 1914. Frictionless checkout status 1914 may be any formof indicator or data designating whether a shopper is eligible forfrictionless checkout. Frictionless checkout status 1914 may be a globalstatus indicating a shopper is always ineligible (e.g., if no paymentinformation has been provided), or may be a temporary status indicatingthe shopper is usually eligible, but that eligibility is temporarilysuspended based on an ineligibility condition. In some embodiments,information regarding an ineligibility condition may be stored as partof frictionless checkout status 1914 (or shopper profile 1910). Forexample, the status may include a description, code, classification, orother information identifying a reason for the status. In someembodiments, the frictionless checkout status 1914 may include otherdata or information, such as whether the shopper is enrolled infrictionless checkout, a frequency or history of use of frictionlesscheckout, or similar information.

Shopper profile 1910 may include any other forms of information that maybe relevant to a shopper, including transaction history 1916.Transaction history 1916 may include a list of historical transactionsassociated with the shopper. In some embodiment, transaction history1916 may be specific to frictionless checkouts, however, it may equallyinclude non-frictionless checkouts. While shopper profile 1910 ispresented graphically in FIG. 19A, it is to be understood that theinformation may be represented in various forms, such as a database, atable, an array, a list, or any other suitable data structure or format.

Once a shopper has been designated as not eligible for frictionlesscheckout, the occurrence of various actions may result in frictionlesscheckout status being granted or restored. In some embodiments, system100 (e.g., via server 135, etc.) may cause implementation of one or moreactions to resolve the ineligibility condition. For example, system 100may determine actions for resolving an ineligibility condition and maycause the actions to be implemented. FIG. 19B is a diagrammaticillustration of various actions that may result in frictionless checkoutstatus being granted or restored, consistent with the disclosedembodiments. In some embodiments, system 100 may cause a query orcommunication to be issued to the shopper to resolve the ineligibilitycondition. For example, a request may be transmitted to a deviceassociated with a shopper, such as devices 145C or 145D.

As described above, the ineligibility condition may be based on adetected ambiguous product interaction event. Accordingly, thecommunication may include a request or instructions for the shopper toresolve the ineligibility condition. In some embodiments, thecommunication may be a request for the shopper to bring one or moreitems within a range of a sensor, as shown as action 1920. For example,in response to the communication, shopper 1820 may bring product 1810within the range of sensor 1922 as shown. In some embodiments, sensor1922 may be a camera, such as camera 1840. Accordingly, sensor 1922 maycapture one or more images for identifying product 1810. As anotherexample, sensor 1922 may be a barcode scanner, which may similarly allowidentification of product 1810 based on a scannable code printed on alabel or otherwise associated with product 1810. As another example,sensor 1922 may be a scale, which may read a weight of product 1810 toresolve the ineligibility condition. For example, an ineligibilitycondition may exist due to a product weight being required to determinepricing and weighing product 1810 may resolve the ineligibilitycondition. Various other forms of sensors may be used as describedherein.

The request may identify the product to be scanned in various ways. Forexample, a request may include instructions relative to the timing of anobject being selected (e.g., “please scan the last item placed in thecart”). As another example, the request may include instructions relatedto particular item type or category (e.g., “please scan the ketchupbottle,” etc.). In some embodiments, the request may include otherinformation, such as a location of a suggested scanner to use,directions to the nearest scanner within the retail environment, anindication that the shopper is currently ineligible for frictionlesscheckout, a description or other indication of the ineligibilitycondition (e.g., that the item was unrecognized, that it was unclearwhether an item was placed in the cart, etc.), or various otherinformation as described herein.

As another example, a communication to a shopper may include a query toidentify products in a shopping cart associated with the shopper. Forexample, the query may request that a shopper confirm that a particularitem or item type was selected, as shown in action 1930. Shopper 1920may receive a request via a user device 1932 to identify or confirm anidentity of product 1810 placed in cart 1922. In some embodiments, thismay include a specific product involved in a detected ambiguous productinteraction event involving the shopper, as described above. Forexample, the query may provide product details most likely associatedwith product 1810 based on information gathered by one or more sensors,and confirmation from the shopper may resolve the uncertainty. Productdetails may include a type of product (e.g., condiment, ketchup, etc.),a brand name, a size, a product subtype (e., flavor, color, model,etc.), a quantity, a price, or other information associated with aproduct. The query may request that the shopper confirm and/or provideone or more of these details. In some embodiments, multiple best matchesfor product information may be presented and the user may select fromthe presented options. In instances where the correct productinformation is not presented (or where no best matches are provided atall), a user may input the correct information (either manually, byscanning the product, capturing an image of the product, etc.) throughuser device 1932. In some embodiments, the query may relate to othertypes of actions performed by a shopper, such as a query relating to atype of product detected as being returned to or removed from a retailshelf. User device 1932 may be any form of device capable of receivingand presenting communications to a user. In some embodiments, userdevice 1932 may correspond to any form of user device described herein,including devices 145C and 145D.

In instances where an ineligibility condition includes a lack of anavailable form of automatic payment, the communication to the shoppermay be to input, select, or confirm one or more automatic paymentoptions. For example, shopper profile 1910 may not include any paymentinformation 1912 and a request may be sent to user device 1932 to inputpayment information. When payment information 1912 is already input, therequest may be for the shopper to select or confirm an automatic paymentmethod to be used for frictionless checkout, update or add paymentinformation (e.g., an expiration date, a security code, etc.), verifythe shopper would like to use frictionless checkout, or the like. Insome embodiments, communications regarding payment information may betransmitted to third parties, such as a bank or financial institutionassociated with the shopper. For example, the communication may requestthat the bank or financial institution provide or confirm automaticpayment information for the shopper. As another example, an action forresolving issues with automatic payment information may includeinterrogating an electronically readable payment instrument associatedwith a shopper. As used herein, an electronically readable paymentinstrument may include any form of device or apparatus that mayelectronically store payment information that may be accessedelectronically. This may include a radio-frequency identification (RFID)chip, a credit card, a mobile phone or device, a wearable device, or anyother device that may provide payment information in response to aninterrogation communication.

In addition to or as an alternative to the communications describedabove for shoppers, similar communications may be generated for storeassociates for resolving an ineligibility condition, as shown by action1940. For example, a communication may be received by store associate1942 via a user device 1944. In some embodiments, user device 1944 maycorrespond to devices 145C and/or 145D. Accordingly, any embodiments ordetails described above with respect to with devices 145C and/or 145Dmay equally apply to user device 1944. The communication may includeinstructions to confirm an identity of one or more products in ashopping cart associated with a shopper. For example, the instructionsmay query the store associate to input or confirm product details asdescribed above. In some embodiments, the communication may provideinstructions to capture an image of the product, scan the product, weighthe product, or capture other information about the product using asensor. In instances where an ambiguous product interaction eventincludes an uncertainty as to which shopper of multiple shoppersselected an item, the request may be to identify or confirm whichshopper selected a particular item. In some embodiments, thecommunication may include other information, such as a shelf the productwas retrieved from (e.g., a shelf number, a camera identifier, etc.), alocation in the retail environment the product was selected from (e.g.,presented as a store map with a visual location identifier, aislenumber, etc.), known or predicted product details, shopper identityinformation (e.g., extracted from shopper profile 1910), or any otherinformation relevant to resolving an ambiguous product interactionevent. Communications may similarly be provided to store associates forresolving other ineligibility conditions. For example, the storeassociate may receive instructions to request identity information froma shopper, confirm a shopper's identity, request or confirm paymentinformation from a shopper, request or confirm enrollment infrictionless checkout, or requests to acquire or confirm any otherinformation that may resolve an ineligibility condition.

In some embodiments, actions taken with respect to one shopper mayresolve ineligibility conditions for other shoppers. For example, insome embodiments, it may be unclear whether an item was taken by a firstshopper or a second shopper, as discussed above. Accordingly, resolutionof the ineligibility condition with respect to a first shopper may alsoresolve the ineligibility condition for the second shopper. For example,if system 100 can confirm product 1810 was placed in shopping cart 1822,this may also confirm it was not placed in shopping cart 1832, therebyresolving the ineligibility condition for shopper 1830 in addition toshopper 1820. In some embodiments, where it is confirmed product 1810was not placed in shopping cart 1822, system 100 may automaticallyconclude it was placed in shopping cart 1832 and resolve theineligibility condition for both shoppers. In other embodiments, system100 may maintain the ineligibility condition for shopper 1830 until itcan be confirmed product 1810 was placed in shopping cart 1832. Any ofthe various actions described above for one shopper (e.g., shopper 1820)may be used to resolve ineligibility conditions for a second shopper(e.g., shopper 1830). Various other actions may also resolve theineligibility condition for the second shopper. For example, if anambiguous product interaction event involving shoppers 1820 and 1830 isdetected, completion of a non-frictionless checkout for shopper 1820(thereby verifying the products selected by shopper 1820) may resolve anineligibility condition for shopper 1830. While various examples areprovided above, any other form of action associated with shopper 1820that confirms whether shopper 1820 selected product 1810 may alsoresolve an ineligibility condition for shopper 1830.

FIG. 20A is a flowchart shopping an exemplary method for addressing ashopper's eligibility for frictionless checkout, consistent with thepresent disclosure. Process 2000A may be performed by at least oneprocessing device of a server, such as processing device 302, asdescribed above. In some embodiments, some or all of process 2000A maybe performed by a different device associated with system 100. In someembodiments, a non-transitory computer readable medium may containinstructions that when executed by a processor cause the processor toperform process 2000A. Further, process 2000A is not necessarily limitedto the steps shown in FIG. 20A, and any steps or processes of thevarious embodiments described throughout the present disclosure may alsobe included in process 2000A, including those described above withrespect to FIGS. 18, 19A, and 19B.

In step 2010, process 2000A includes identifying at least one shopper ina retail store designated as not eligible for frictionless checkout.Referring to FIG. 18 , this may include identifying shopper 1820, whichmay be designated as not eligible for frictionless checkout, asdescribed above. In some embodiments, the identification of the at leastone shopper in the retail store may be based on analysis of at least oneimage captured by a camera in the retail store. For example, this mayinclude analysis of an image captured by camera 1840. In someembodiments, the identification of the at least one shopper may furtherbe based on shopper profile information stored in at least one database.For example, this may include information stored in a shopper profileassociated with the at least one shopper, such as shopper profile 1910described above. In some embodiments, the information stored in theshopper profile may indicate whether the at least one shopper has anavailable automatic payment method. For example, this may includeautomatic payment information 1912 described above. According to someembodiments, the identification of the at least one shopper may be basedon a detected ambiguous product interaction event involving the at leastone shopper. For example, step 2010 may include detecting the ambiguousproduct interaction event and identifying the at least one shopper asbeing designated as not eligible for frictionless checkout based on thedetected ambiguous product interaction event.

In step 2012, process 2000A includes automatically identifying anineligibility condition associated with the at least one shopper'sdesignation as not eligible for frictionless checkout. Step 2012 may beperformed in response to the identification of the at least one shopperdesignated as not eligible for frictionless checkout of step 2010. Asdescribed above, the ineligibility condition may include various typesof conditions. In some embodiments, the ineligibility condition mayinclude uncertainty relative to a product selection by the at least oneshopper due to a detected ambiguous product interaction event involvingthe at least one shopper, as described above with respect to FIG. 18 .In some examples, image data captured using one or more images sensors(such as camera 1840) may be analyzed to identify the ineligibilitycondition associated with the at least one shopper's designation as noteligible for frictionless checkout. For example, positions of the atleast one shopper may be detected in the image data, and theineligibility condition may be identified based on the detectedpositions (for example, based on the positions being too close to oneanother, based on the positions of at least two shoppers being in aselected region (the selected region may be selected based on shelf 1802and/or product 1810), based on the positions being at an angulardistance with respect to the image sensor that is lower than a selectedthreshold, etc.). In another example, one or more actions of the atleast one shopper may be detected in the image data (for example usingan action recognition algorithm), and the ineligibility condition may beidentified based on the one or more actions. In yet another example, aconvolution of at least part of the image data may be calculated, inresponse to a first value of the convolution of the at least part of theimage data, a first ineligibility condition may be identified, and inresponse to a second value of the convolution of the at least part ofthe image data, a second ineligibility condition may be identified, thesecond ineligibility condition may differ from the first ineligibilitycondition. Alternatively or additionally, the ineligibility conditionmay include a lack of an available form of automatic payment for the atleast one shopper.

In step 2014, process 2000A includes determining one or more actions forresolving the ineligibility condition. In some embodiments, the one ormore actions include issuing a query to the at least one shopper toconfirm an identity of products in a shopping cart associated with theat least one shopper. For example, this may include a query for theshopper to use a dedicated device for scanning a barcode or placing theproducts in front of a camera. Alternatively or additionally, this mayinclude a request to identify or confirm selection of an item. In someembodiments, the query may identify a specific product involved in adetected ambiguous product interaction event involving the at least oneshopper. For example, the query may identify a specific product orproduct type believed to have been interacted with by the shopper. Insome embodiments, the query may relate to a type of product detected asbeing removed from or returned to a retail shelf.

According to some embodiments, the one or more actions may includeautomatically generating a communication to the at least one shopperrequesting that the at least one shopper bring one or more selecteditems in range of at least one sensor. For example, the sensor mayinclude a camera, a barcode scanner, a scale or various other forms ofsensors described herein. In some embodiments, the one or more actionsmay include automatically generating a communication to the at least oneshopper indicating that the at least one shopper is currently ineligiblefor frictionless checkout. For example, the communication may include anindication of the ineligibility, an indication of a reason for theeligibility, at least one remedial action for the at least one shopperto take to resolve the ineligibility condition, or any other relevantinformation. In some embodiments, the one or more actions may involvegenerating communications for other entities, such as store associates.For example, the one or more actions include automatically generating acommunication to a store associate with instructions to confirm anidentity of one or more products in a shopping cart associated with theat least one shopper.

In embodiments where the ineligibility condition includes a lack of anavailable form of automatic payment for the at least one shopper, theone or more actions may include steps to obtain information regardingautomatic payment. For example, the one or more actions include sendingof an electronic communication to the at least one shopper regardingautomatic payment options. This may include sending a communication todevice 1932 as described above. Similarly, the one or more actions mayinclude interrogating an electronically readable payment instrumentassociated with the at least one shopper. As another example, the one ormore actions may include sending of an electronic communication to abank associated with the at least one shopper.

In step 2016, process 2000A includes causing implementation of the oneor more actions for resolving the ineligibility condition. This mayinclude generating and transmitting the various queries or othercommunications described above. In some embodiments, this may includegenerating instructions to be performed by a device, such as a personaldevice, a camera, a scanner, a sensor, or other device to perform aparticular operation, such as scanning a product, or the like.

In some embodiments, process 2000A may further include steps to resolvethe identified ineligibility condition. For example, in step 2018,process 2000A includes receiving an indication of successful completionof the one or more actions. This may include any form of informationcollected in response to the various actions described above. Forexample, this may include receiving an indication that a shopper orstore associate has confirmed the identity of a product or that aparticular interaction with a product has occurred. As another example,this may include receiving information from a camera or other sensorassociated with the one or more actions described above.

In step 2020, in response to receipt of the indication of successfulcompletion of the one more actions, process 2000A includes generating astatus indicator indicating that the at least one shopper is eligiblefor frictionless checkout and storing the generated status indicator ina memory. For example, this may include generating or modifyingfrictionless checkout status 1914 stored in shopper profile 1910. Thismay include updating other information, such as a database, a table, anarray, or other data structure. In some embodiments, step 2020 mayinclude generating and transmitting a communication indicating theeligibility status. For example, information indicating the status maybe transmitted to device 1932 associated with a shopper, device 1944associated with a store associate, or various other devices describedherein.

As described above, in some embodiments, an ineligibility condition mayarise due to an ambiguous product interaction event involving multipleshoppers. FIG. 20B is a flowchart showing another exemplary method foraddressing a shopper's eligibility for frictionless checkout, consistentwith the present disclosure. Process 2000B may be performed by at leastone processing device of a server, such as processing device 302, asdescribed above. In some embodiments, some or all of process 2000B maybe performed by a different device associated with system 100. In someembodiments, a non-transitory computer readable medium may containinstructions that when executed by a processor cause the processor toperform process 2000B. Further, process 2000B is not necessarily limitedto the steps shown in FIG. 20B, and any steps or processes of thevarious embodiments described throughout the present disclosure may alsobe included in process 2000B, including those described above withrespect to FIGS. 18, 19A, 19B, and 20A.

In step 2030, process 2000B includes receiving output from at least onesensor positioned in a retail store. For example, the sensor may includean image capture device, such as camera 1840. The sensor may includeother types of sensors, such as a barcode scanner, a scale, a pressuresensor, or other sensors that could potentially be associated withdetecting an ambiguous product interaction event.

In step 2032, process 2000B includes analyzing the first data to detectan ambiguous product interaction event involving a first shopper and asecond shopper. For example, this may include detecting an ambiguousproduct interaction event in which it is unclear whether a product wasselected by the first shopper or the second shopper, as described ingreater detail above with respect to FIG. 18 . In some examples, imagedata captured using one or more images sensors (such as camera 1840) maybe analyzed to detect the ambiguous product interaction event involvingthe first shopper and the second shopper. For example, positions of thefirst shopper and the second shopper may be detected in the image data,and the ambiguous product interaction event may be detected based on thedetected positions (for example, based on the positions being too closeto one another, based on the positions of the first shopper and thesecond shopper being in a selected region (the selected region may beselected based on shelf 1802 and/or product 1810), based on thepositions being at an angular distance with respect to the image sensorthat is lower than a selected threshold, etc.). In another example, oneor more actions of the first shopper and the second shopper may bedetected in the image data (for example using an action recognitionalgorithm), and the ambiguous product interaction event may be detectedbased on the one or more actions. In yet another example, a convolutionof at least part of the image data may be calculated, in response to afirst value of the convolution of the at least part of the image data,ambiguous product interaction event may be detected, and in response toa second value of the convolution of the at least part of the imagedata, the detection of the ambiguous product interaction event may bewithheld and/or forgone.

In step 2034, process 2000B includes designating both the first shopperand the second shopper as ineligible for frictionless checkout inresponse to detection of the ambiguous product interaction event. Forexample, this may include updating a frictionless checkout status 1914in shopper profiles of the first shopper and the second shopper. Thismay further include generating a communication to transmit to a deviceassociated with the first shopper and/or the second shopper indicatingthe ineligibility status, as described above. In some embodiments, thismay further include determining one or more actions for resolving theineligibility condition and causing implementation of the one or moreactions for resolving the ineligibility condition, as described abovewith respect to process 2000A.

In step 2036, process 2000B includes detecting an action taken by thefirst shopper, wherein the action enables resolution of ambiguityassociated with the product interaction event. For example, the actiontaken by the first shopper may include a non-frictionless checkout.Accordingly, during the non-frictionless checkout an accurate inventoryof items selected and purchased by the first shopper may be obtained,thus resolving the ambiguity associated with the product interactionevent for the second shopper. As another example, the action taken bythe first shopper may include scanning one or more selected items orbringing one or more selected items within range of at least one sensor,such as a camera, a scale, or the like. Accordingly, based oninformation obtained from the scanner or sensor, the selection of aproduct by the first shopper may be confirmed.

In step 2038, process 2000B includes designating the second shopper aseligible for frictionless checkout in response to detection of theaction taken by the first shopper. For example, this may includeupdating a frictionless checkout status 1914 in a shopper profile forthe second shopper, as described above. Step 2038 may also includedesignating the first shopper as eligible for frictionless checkout inresponse to detection of the action taken by the first shopper. Step2038 may further include generating a communication indicating theineligibility condition has been resolved, which may be transmitted toone or more of the first shopper, the second shopper, a store associate,or other entities associated with system 100.

As discussed above, one common way of improving customer experiencewhile shopping in retail store has been to provide self-checkoutcounters in a retail store, allowing shoppers to quickly purchase theirdesired items and leave the store without needing to wait for a storeassociate to help with the purchasing process. The disclosed embodimentsprovide another method of improving customer experience in the form offrictionless checkout, particularly for pay-by-weight products.

As noted generally above, frictionless checkout refers to any checkoutprocess for a retail environment with at least one aspect intended toexpedite, simplify, or otherwise improve an experience for customers. Insome embodiments, frictionless checkout may reduce or eliminate the needto take inventory of products being purchased by the customer atcheckout. For example, this may include tracking the selection ofproducts made by the shopper so that they are already identified at thetime of checkout. The tracking of products may occur through theimplementation of sensors used to track movement of the shopper and/orproducts within the retail environment, as described throughout thepresent disclosure. Additionally or alternatively, frictionless checkoutmay include an expedited or simplified payment procedure. For example,if a retail store has access to payment information associated with ashopper, the payment information may be used to receive payment forproducts purchased by the shopper automatically or upon selection and/orconfirmation of the payment information by the shopper. In someembodiments, frictionless checkout may involve some interaction betweenthe shopper and a store associate or checkout device or terminal. Inother embodiments, frictionless checkout may not involve any interactionbetween the shopper and a store associate or checkout device orterminal. For example, the shopper may walk out of the store with theselected products and a payment transaction may occur automatically.While the term “frictionless” is used for purposes of simplicity, it isto be understood that this encompasses semi frictionless checkouts aswell. Accordingly, various types of checkout experiences may beconsidered “frictionless,” and the present disclosure is not limited toany particular form or degree of frictionless checkout.

Frictionless shopping of pay-by-weight products, for example, productsfrom a deli department, products such as specialty coffee, tea, orcheese sold by weight, or other pay-by-weight, clerk assistedtransactions may differ significantly from frictionless shopping ofregular products. This is because each pay-by-weight product may differ(for example, in weight, in content, in price, and so forth). Even whendedicated cameras are used to capture product-customer transactions, anambiguity may arise when a number of shoppers order and receivepay-by-weight products at the same time. In such situations, feedbackfrom a store associate may assist in resolving the ambiguity. Thedisclosed methods and systems may provide a way for updating the virtualshopping carts of shoppers with pay-by-weight products.

In some embodiments, a non-transitory computer-readable medium mayinclude instructions that when executed by a processor may cause theprocessor to perform a method for updating virtual shopping carts ofshoppers with pay-by-weight products. For example, as discussed above,the disclosed system may include one or more servers 135, which mayinclude one or more processing devices 202. Processing device 202 may beconfigured to execute one or more instructions stored in anon-transitory computer-readable storage medium. As also discussedabove, the non-transitory computer-readable medium may include one ormore of random access memory (RAM), read-only memory (ROM), volatilememory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives,disks, any other optical data storage medium, any physical medium withpatterns of holes, a RAM, a PROM, an EPROM, a FLASH-EPROM or any otherflash memory, NVRAM, a cache, a register, any other memory chip orcartridge, and networked versions of the same, etc.

FIG. 21 illustrates an example of one or more shoppers interacting witha store associate to purchase a pay-by-weight product in a retail store(e.g., 105A, 105B, 105C, etc.). As illustrated in FIG. 21 , one or moreof shoppers 2112, 2114, 2116 may interact with store associate 2102 topurchase one or more of products 2122, 2124, 2126, etc. It iscontemplated that the instructions stored on the non-transitorycomputer-readable medium may allow processing device 202 to update avirtual shopping cart associated with a shopper (e.g., shopper 2112,2114, 2116, etc.) by including one or more pay-by-weight products (e.g.,products 2122, 2124, 2126, etc.) in the virtual shopping cart.

In some embodiments, the method may include receiving one or more imagescaptured by one or more image sensors, wherein the one or more imagesmay depict product interactions between a store associate and aplurality of shoppers, wherein each of the product interactions mayinvolve at least one pay-by-weight product. For example, as discussedabove, a retail store (e.g., 105A, 105B, 105C, etc., see FIG. 1 ) mayinclude one or more capturing devices 125 configured to capture one ormore images. Capturing devices 125 may include one or more of a digitalcamera, a time-of-flight camera, a stereo camera, an active stereocamera, a depth camera, a Lidar system, a laser scanner, CCD baseddevices, etc. Capturing devices 125 may be stationary or movable devicesmounted to walls or shelves in the retail stores (e.g., 105A, 105B,105C, etc.). It is also contemplated that capturing devices 125 may behandheld devices (e.g., a smartphone, a tablet, a mobile station, apersonal digital assistant, a laptop, etc.), a wearable device (e.g.,smart glasses, a smartwatch, a clip-on camera, etc.) or may be attachedto a robotic device (e.g., drone, robot, etc.). It is furthercontemplated that capturing devices 125 may be held or worn by ashopper, a store employee, or by one or more other persons present inretail stores 105.

One or more of capturing devices 125 may include one or more imagesensors 310, which may include one or more semiconductor charge-coupleddevices (CCD), active pixel sensors in complementarymetal-oxide-semiconductor (CMOS), or N-type metal-oxide-semiconductors(NMOS, Live MOS), etc. The one or more image sensors 310 in retailstores 105 may be configured to capture images of one or more persons(e.g., shoppers, store associates, etc.), one or more pay-by-weightproducts (e.g., 2122, 2124, 2126, 2142, 2144, 2146, etc.), and/or otherobjects (e.g., shopping carts, checkout counters, walls, columns, poles,aisles, pathways between aisles), etc. The images may be in the form ofimage data, which may include, for example, pixel data streams, digitalimages, digital video streams, data derived from captured images, etc.

The one or more images obtained by the one or more image sensors 310 maydepict one or more product interactions. Product interactions mayinclude one or more actions of shopper (e.g., 2112, 2114, 2116, etc.)and/or store associate (e.g., 2102) to receive one or more pay-by-weightproducts (e.g., 2122, 2124, 2126, 2142, 2144, 2146, etc.) or to returnone or more pay-by-weight products to the store associate. It iscontemplated that one or more of these product interactions may beassociated with pay-by-weight products. Pay-by-weight products mayinclude products that may be sold by the retail store by weight or byquantity and may require interaction between a shopper and a storeassociate for purchasing the pay-by-weight products. Examples of apay-by-weight product may include deli-meats (e.g., 2122), deli-cheeses(e.g., 2124), breads, baked goods (e.g., donuts, bagels, cup cakes,etc.), cigarettes, high-priced wines or liquor, etc. A retailer mayrequire a store associate 2102 to provide the pay-by-weight products toshoppers (e.g., 2112, 2114, 2116, etc.) instead of allowing a shopper toremove the pay-by-weight product from a store shelf on their own.

In some embodiments, the method may include analyzing the one or moreimages to identify the product interactions and to associate the atleast one pay-by-weight product involved with each product interactionwith a particular shopper among the plurality of shoppers. For example,processing device 202 may analyze image data obtained by the one or moreimage sensors 310 to identify one or more persons or objects in theimage data. As used herein, the term identify may broadly refer todetermining an existence of a person or a product in the image data. Itis also contemplated, however, that in some embodiments identifying aperson in the image data may include recognizing a likeness of theperson and associating an identifier (e.g., name, customer ID, accountnumber, telephone number, etc.) with the recognized person. It iscontemplated that processing device 202 may use any suitable imageanalysis technique, for example, including one or more of objectrecognition, object detection, image segmentation, feature extraction,optical character recognition (OCR), object-based image analysis, shaperegion techniques, edge detection techniques, pixel-based detection,artificial neural networks, convolutional neural networks, etc., toidentify one or more persons or objects in the image data. It is furthercontemplated that processing device 202 may access one or more databases140 to retrieve one or more reference images of likenesses of one ormore persons or reference images of one or more pay-by-weight products.Further, processing device 202 may use one or more of the image analysistechniques discussed above to compare the images retrieved from database140 with the image data received from the one or more image sensors 310to recognize the likeness of one or more shoppers (e.g., 2112, 2114,2116, etc.) or to recognize one or more pay-by-weight products (e.g.,2122, 2124, 2126, 2142, 2144, 2146, etc.) in the image data. It is alsocontemplated that processing device 202 may retrieve other identifyinginformation (e.g., name, customers ID, account number, telephone number,etc.) associated with the images retrieved from database 140 based on,for example, profiles of the one or more shoppers (e.g., 2112, 2114,2116, etc.) stored in database 140. It is further contemplated thatprocessing device 202 may retrieve information (e.g., product id, price,brand name, etc.) about the one or more pay-by-weight products.

Processing device 202 may associate the at least one pay-by-weightproduct (e.g., 2142) involved with each product interaction with aparticular shopper (e.g., 2114) from among all the shoppers (e.g., 2112,2114, 2116, etc.) present in the analyzed images. For example,processing device 202 may recognize that shopper 2114 receivedpay-by-weight product 2142 from store associate 2102 in a particularproduct interaction. Alternatively, processing device 202 may recognizethat shopper 2114 returned pay-by-weight product 2142 to store associate2102 in a product interaction. Processing device 202 may associatepay-by-weight product 2142 involved with that particular productinteraction with a particular shopper 2114. It is to be understood thatshopper 2114 and pay-by-weight product 2142 are merely exemplary andthat processing device 202 may perform the above-describe associationbetween any of shoppers (e.g., 2112, 2114, 2116, etc.) and any ofpay-by-weight products (e.g., 2122, 2124, 2126, 2142, 2144, 2146, etc.)

In some embodiments, the method may include providing a notification tothe store associate requesting supplemental information to assist in theassociation of the at least one pay-by-weight product involved with aselected product interaction with the particular shopper among theplurality of shoppers. As discussed above, processing device 202 maydetect one or more product interactions based on an analysis of imagedata obtained by the one or more image sensors 310. It is contemplated,however, that in some instances, processing device 202 may not be ableto identify the shopper and/or the pay-by-weight product associated witha product interaction, because of, for example, a quality of the imagedata. For example, in some instances images obtained by the one or moresensors 310 may be too dark because of insufficient light. As a result,processing device 202 may not be able to identify shopper (e.g., 2114)and/or pay-by-weight product (e.g., 2142) being given to shopper 2114 bystore associate 2102. As another example, portions of an image ofshopper 2114 and/or pay-by-weight product 2142 may be occluded byanother shopper (e.g., 2116), and/or another object. By way of anotherexample, an image of product (e.g., 2122, 2124, 2126, 2142, 2144, 2146,etc.) may be blurry or out of focus making it difficult to read a labelon the product using optical character recognition techniques. As aresult, processing device 202 may not be able to identify shopper (e.g.,2112, 2114, 2116, etc.) and/or pay-by-weight product (e.g., 2122, 2124,2126, 2142, 2144, 2146, etc.) based on analysis of the images, and/orprocessing device 202 may not be able to determine whether, for example,product 2142 was given to shopper 2114 or 2116. In such situations,processing device 202 may provide a notification to store associate2102, requesting supplemental information that may assist processingdevice 202 in identifying, for example, which of shopper 2114 or 2116may have received product 2142. Processing device 202 may also be ableto use the supplemental information to associate product (e.g., 2142)with shopper (e.g., 2114).

In some embodiments, the notification may be provided to the storeassociate when the analysis of the one or more images results in anambiguity level greater than a predetermined threshold with respect tothe association of the at least one pay-by-weight product involved witheach product interaction with the particular shopper among the pluralityof shoppers. For example, processing device 202 may be configured todetermine an ambiguity level associated with the one or more productinteractions when processing device 202 is unable to identify a shopper(e.g., 2112, 2114, 2116, etc.) and/or a pay-by-weight product (e.g.,2122, 2124, 2126, 2142, 2144, 2146, etc.), or when processing device 202is unable to identify a shopper (e.g., 2114 or 2116) to whom a product(e.g., 2142) may have been given during a product interaction. By way ofexample, the ambiguity level associated with a product interaction maybe a numerical value ranging between a minimum and maximum value, withthe value indicating a degree of ambiguity. As another example, theambiguity level may be in the form of text (e.g., Low, Medium, High,etc.) indicating a degree of ambiguity. Processing device 202 may beconfigured to identify shopper (e.g., 2112, 2114, 2116, etc.) and/orpay-by-weight product (e.g., 2122, 2124, 2126, 2142, 2144, 2146, etc.)by comparing image data obtained from the one or more image sensors 310with one or more reference images of shopper (e.g., 2112, 2114, 2116,etc.) and/or pay-by-weight product (e.g., 2122, 2124, 2126, 2142, 2144,2146, etc.). Processing device 202 may be configured to determine anambiguity level associated with a product interaction based on, forexample, a degree of similarity between the image data and the referenceimage of the (e.g., 2112, 2114, 2116, etc.) and/or pay-by-weight product(e.g., 2122, 2124, 2126, 2142, 2144, 2146, etc.). It is alsocontemplated that processing device 202 may execute one or moremathematical or statistical algorithms, machine learning or neuralnetwork models, and/or other models to determine an ambiguity levelassociated with a product interaction.

In some embodiments, processing device 202 may transmit a notificationto store associate 2102 when an ambiguity level associated with aproduct interaction is greater than a predetermined threshold ambiguitylevel. A product interaction having an ambiguity level greater than thethreshold ambiguity level may be deemed an ambiguous productinteraction. It is contemplated that processing device 202 may comparethe determined ambiguity level associated with a product interactionwith the threshold ambiguity level. For example, processing device 202may be configured to determine that the product interaction betweenstore associate 2102 and shopper 2114 or 2116 is ambiguous when thedetermined ambiguity level is greater than or equal to the thresholdambiguity level. When processing device 202 determines that thedetermined ambiguity level is greater than or equal to the thresholdambiguity level, processing device 202 may be configured to transmit anotification to store associate 2102 to provide supplementalinformation.

In some embodiments, the threshold may be determined based on priceassociated with the at least one pay-by-weight product. For example, aretailer may determine that there is no need to request supplementalinformation form store associate 2102 for product interactions involvinglow-priced items (e.g., generic brands of meat or cheese). The retailermay do so to prevent overwhelming store associate 2102 with too manyrequests for supplemental information and/or to allow other tasksassigned to store associate 2102 to take priority. In such cases,processing device 202 may set a high threshold ambiguity level forlow-priced items. As a result, processing device 202 may not providenotifications to store associate 2102 for product interactionsassociated with low-priced items and having an ambiguity level less thanthe high threshold ambiguity level. On the other hand, the retailer maywish to make sure that high-priced pay-by-weight products (e.g., rarecaviar, premium deli meat or cheese, very old vintage wine, etc.) areprovided to the correct shopper. In such cases, processing device 202may set a low threshold ambiguity level for high-priced items. This mayensure that even product interactions associated with high-priced itemsand having an ambiguity level less than the low threshold ambiguitylevel are detected by processing device 202. As a result, processingdevice 202 may transmit notifications to store associate 2102 forproduct interactions associated with high-priced items having arelatively low ambiguity level.

In some embodiments, the threshold may be determined based on at leastone pending task of the store associate. In some situations, thethreshold ambiguity level may be based whether store associate 2102 isbusy. A retailer may determine that it is not cost-effective to askstore associate 2102 to provide supplemental information when storeassociate 2102 may be busy, for example, for a low-priced pay-by-weightproduct involved in an ambiguous product interaction. In such cases,processing device 202 may set a high threshold ambiguity level forlow-priced pay-by-weight products. As a result, when processing device202 determines that store associate 202 has a pending task, processingdevice 202 may be configured to transmit a notification to storeassociate only for the very ambiguous product interactions involvinglow-priced items (e.g., product interactions having a relatively highambiguity level, e.g., 90% or higher). Processing device 202 may notrequire information from store associate 2102 for product interactionsassociated with low-priced items and having a relatively low ambiguitylevel (e.g., 50% or lower).

In contrast, a retailer may wish to make sure that high-pricedpay-by-weight products (e.g., rare caviar, premium deli meat or cheese,very old vintage wine, etc.) are provided to the correct shopper evenwhen the store associate may be busy. In such cases, processing device202 may set a low threshold ambiguity level for high-pricedpay-by-weight products. As a result, when processing device 202determines that store associate 2102 has a pending task, processingdevice 202 may be configured to transmit a notification to storeassociate 2102 for product transactions involving high-priced items eventhough these transactions may have a relatively low ambiguity level(e.g., 50% or less).

It is contemplated that the notification to store associate 2102 mayrequest store associate 2102 to identify a shopper (e.g., 2114 or 2116)receiving a pay-by-weight product (e.g., 2142). Additionally oralternatively, the notification to store associate 2102 may requeststore associate 2102 to identify a pay-by-weight product (e.g., 2142)given to a particular shopper (e.g., shopper 2114). Store associate 2102may identify a shopper and/or a pay-by-weight product in many ways. Insome embodiments, the notification provided to the store associate mayinclude a request to match a shopper to a specific pay-by-weightproduct, and the supplemental information includes a gesture by thestore associate represented in additional images captured by the one ormore image sensors, wherein the gesture enables identification of theshopper that received the specific pay-by-weight product. It iscontemplated that in some situations, store associate 2102 may identifya shopper (e.g., 2112, 2114, 2116, etc.) and/or a pay-by-weight product(e.g., 2122, 2124, 2126, 2142, 2146, 2148, etc.) using a gesture (e.g.,pointing to the shopper, pointing to the product, etc.). As discussedabove, processing device 202 may receive one or more images obtained bythe one or more imaging sensor 310. Processing device 202 may beconfigured to perform image analysis, using one or more of thetechniques or algorithms discussed above, to identify a gesture made bystore associate 2102 in the image data obtained by the one or more imagesensor 310. Processing device 202 may also be configured to identify ashopper (e.g., 2112, 2114, 2116, etc.) and/or a pay-by-weight product(e.g., 2122, 2124, 2126, 2142, 2146, 2148, etc.) based on the identifiedgesture. For example, when a gesture made by store associate 2102includes pointing to, for example shopper 2114, processing device 202may be configured to associate shopper 2114 with an ambiguous productinteraction and/or with a particular pay-by-weight product (e.g., 2142).By way of another example, when a gesture made by store associate 2102includes pointing towards a pay-by-weight product (e.g., 2142),processing device 202 may be configured to associate that pay-by-weightproduct 2142 with an ambiguous product interaction and/or with aparticular shopper (e.g., 2114).

In some embodiments, the supplemental information may be provided by thestore associate via a computing device. It is contemplated that storeassociate 2102 may additionally or alternatively identify a shopper(e.g., 2112, 2114, 2116, etc.) and/or a pay-by-weight product (e.g.,2122, 2124, 2126, 2142, 2146, 2148, etc.) by selecting an identifierassociated with the shopper and/or with the pay-by-weight product usinga computing device. For example, processing device 202 may transmit anotification to a computing device associated with store associate 2102.It is contemplated that the computing device may include one or more ofa desktop computer, a laptop computer, a checkout terminal, etc. In someembodiments, the computing device may be a mobile computing device. Forexample, the computing device may include a cellular phone, asmartphone, a tablet computer, a wearable device such as a smartwatch,etc.

In some embodiments, the notification to the store associate may includeidentifiers of two or more shoppers and may request that the storeassociate select which of the two or more shoppers received a specificpay-by-weight product. As discussed above, processing device 202 maytransmit a notification to a computing device associated with storeassociate 2102, requesting supplemental information regarding a shopper(e.g., 2112, 2114, 2116, etc.) and/or a pay-by-weight product (e.g.,2122, 2124, 2126, 2142, 2146, 2148, etc.) associated with an ambiguousproduct interaction. It is contemplated that in some embodiments thenotification transmitted by processing device 202 to the computingdevice associated with store associate 2102 may include identifiers oftwo or more shoppers. In some embodiments, the identifiers may includepictures of the two or more shoppers. By way of example, the identifiersmay include photographs of the two or more shoppers. In someembodiments, the identifiers may include shopper id numbers. Forexample, identifiers may include names, customer identification numbers,and/or any other type of identification numbers associated with the twoor more shoppers. Processing device 202 may transmit a notification tostore associate 2102, with the identifiers displayed on, for example, acomputing device associated with store associate 2102.

FIG. 22 illustrates an example of a computing device 2210 associatedwith store associate 2102 displaying a notification sent to storeassociate 2102 by processing device 202. For example, device 2210 may bea smartphone having a display 2220. As illustrated in FIG. 22 , images2202 and 2204 of a pair of shoppers associated with an ambiguous productinteraction may be displayed on display 2220. Additionally, a graphicalelement 2230 (e.g., button, widget, etc.) may be displayed below each ofthe images 2202 and 2204. Store associate 2102 may be able to selectgraphical element 2230 below one of the displayed images, using one ormore input devices associated with device 2210, to provide supplementalinformation, identifying a shopper associated with the ambiguous productinteraction. Although FIG. 22 illustrates images of shoppers displayedon display 2220 of device 2210, it is contemplated that in someembodiments, additionally or alternatively, images of one or morepay-by-weight products (e.g., 2122, 2124, 2126, 2142, 2146, 2148, etc.)associated with an ambiguous product interaction may be displayed ondisplay 2220. Furthermore, one or more graphical elements 2230 may bedisplayed on display 2220 to allow store associate 2102 to identify oneor more of the pay-by-weight products (e.g., 2122, 2124, 2126, 2142,2146, 2148, etc.) associated with the ambiguous product interaction.

In some embodiments, the two or more shoppers may be ranked in thenotification based on a determined likelihood that each of the two ormore shoppers received the specific pay-by-weight product. It iscontemplated that processing device 202 may determine a likelihood thateach of shoppers (e.g., 2112, 2114, 2116, etc.) received a particularpay-by-weight product (e.g., 2142). Processing device 202 may alsotransmit the determined likelihoods together with the identifiers forthe two or more shoppers (e.g., 2112, 2114, 2116, etc.) to computingdevice 2210 associated with store associate 2102. In some embodiments,the likelihood may be determined based on archived shopping behavior forat least one of the two or more shoppers. It is contemplated that server135 and/or database 140 may store information associated with one ormore shoppers (e.g., 2112, 2114, 2116, etc.) in the form of customerprofiles. For example, a customer profile for a shopper (e.g., 2112,2114, 2116, etc.) may include identification information of shopper(e.g., 2112, 2114, 2116, etc.). The identification information mayinclude, for example, a name, an identification number, an address, andtelephone number, an email address, a mailing address. The customerprofile for a shopper (e.g., 2112, 2114, 2116, etc.) may also include,for example, a shopping history, including a list of products previouslypurchased by shopper (e.g., 2112, 2114, 2116, etc.), frequency ofpurchase of each of the products in the list, total value of productspurchased by shopper (e.g., 2112, 2114, 2116, etc.) during each visit toa retail store or during a predetermined period of time, payment historyof shopper (e.g., 2112, 2114, 2116, etc.), including informationregarding on-time payments, late payments, delinquent payments, etc. Insome embodiments, processing device 202 may determine a likelihood thata particular shopper (e.g., 2112, 2114, 2116, etc.) may have received aparticular pay-by-weight product (e.g., 2142) based on the past shoppinghistory of shopper (e.g., 2112, 2114, 2116, etc.) stored in a customerprofile associated with the shopper.

In some embodiments, the likelihood may be determined based on ananalysis of the received one or more images. For example, processingdevice 202 may determine a distance between shopper (e.g., 2112, 2114,2116, etc.) and pay-by-weight product (e.g., 2122, 2124, 2126, 2142,2146, 2148, etc.). Processing device 202 may identify a shopper (e.g.,2112, 2114, 2116, etc.) as having received pay-by-weight product (e.g.,2122, 2124, 2126, 2142, 2146, 2148, etc.) based on the determineddistance. For example, shopper 2114 may be positioned at a distance L₁from pay-by-weight product 2142, whereas shopper 2112 may be positionedat a distance L₂ from pay-by-weight product. Because distance L₁ may besmaller than distance L₂, processing device 202 may determine thatshopper 2114 is more likely to have received pay-by-weight product 2142than shopper 2112. It is contemplated that processing device 202 may useother criteria (e.g., an action or a gesture of a shopper or a storeassociate, matching an order number or a product number with a shopper,etc.) to identify a shopper (e.g., 2112, 2114, 2116, etc.) as havingreceived pay-by-weight product (e.g., 2122, 2124, 2126, 2142, 2146,2148, etc.) based on analysis of the images captured by image sensors310. Additionally or alternatively, processing device 202 may use one ormore mathematical and/or statistical algorithms, one or more machinelearning models, and/or one or more neutral networks to assign alikelihood that shopper (e.g., 2112 or 2114) received pay-by-weightproduct 2142. Processing device 202 may also be configured to rank thelist of identifiers transmitted to, for example, computing device 2210associated with store associate 2102 based on the determined likelihood.For example, processing device 202 may determine that shopper 2114 has a60% likelihood of having received pay-by-weight product 2142 whereasshopper 2112 has 20% likelihood of having received pay-by-weight product2142. Processing device 202 may transmit a notification to computingdevice 2210 associated with store associate 2102 when, for example, thelikelihoods are less than a threshold likelihood (e.g., thresholdlikelihood of 80%). Processing device 202 may be configured to providethe determined likelihood (e.g., 60% or 20%, etc.) in association withthe respective identifiers (e.g., photographs, customer identifiers,etc.) of, for example, shoppers 2112 and 2114 in the notificationtransmitted to computing device 2210 associated with store associate2102. It is contemplated that store associate may rely on the displayedlikelihood values in providing the supplemental information toprocessing device 202. It is to be understood that the values 20%, 60%,80%, etc., are exemplary and nonlimiting and processing device mayassign other numerical or textual values to the determined and thresholdlikelihoods.

In some embodiments, the method may include receiving the requestedsupplemental information from the store associate. For example, whenstore associate 2102 receives a notification from processing device 202,store associate 2102 may identify a shopper (e.g., 2112, 2114, 2116,etc.) and/or pay-by-weight product (e.g., 2122, 2124, 2126, 2142, 2146,2148, etc.). As discussed above, store associate 2102 may do so by usingone or more gestures and/or by making a selection on computing device2210 associated with store associate 2102. As one example, storeassociate 2102 may point to one of the shoppers (e.g., point to shopper2114 out of shoppers 2114 and 2116). Processing device 202 may detectthe pointing gesture based on an analysis of the one or more imagescaptured by image sensors 310. Processing device 202 may receive thesupplemental information in the form of the detected gesture. By way ofanother example, store associate 2102 may select one of the plurality ofimages of shoppers (e.g., 2202) displayed on computing device 2210associated with store associate 2102 to identify the shopper that mayhave received a pay-by-weight product. Processing device 202 may receivea signal from computing device 2210, identifying the shopper selected bystore associate 2102 (e.g., supplemental information).

In some embodiments, the store associate may be a service robot, andwherein the supplemental information may be determined based on anoutput of one or more sensors associated with the service robot.Although the above description has identified store associate 2102 asbeing a person, it is contemplated that in some embodiments the storeassociate may be a service robot (e.g., robot 2104, see FIG. 21 ). Insome embodiments, a service robot may have a human-like appearance andbe capable of mobility (e.g., a legged robot). In other embodiments, aservice robot may take on a machine-like appearance having mobility viaone or more wheels or one or more tracks or treads. It is contemplatedthat service robot 2104 may include a variety of sensors, for example,image sensors, weight sensors, pressure sensors, distance sensors, etc.It is also contemplated that service robot 2104 may be equipped with itsown processing device. When service robot 2104 provides a pay-by-weightproduct (e.g., 2122, 2124, 2126, 2142, 2146, 2148, etc.) to a shopper(e.g., 2112, 2114, 2116, etc.), or receives a pay-by-weight product froma shopper, the one or more sensors of service robot 2104 may generatesignals that may be transmitted by service robot 2104 to processingdevice 202. Processing device 202 may be configured to determine whethera pay-by-weight product (e.g., 2122, 2124, 2126, 2142, 2146, 2148, etc.)has been provided by or received by service robot 2104 based on thesignals received from the one or more sensors of service robot 2104.Processing device 202 may also be configured to identify a shopper(e.g., 2112, 2114, 2116, etc.) and/or pay-by-weight product (e.g., 2122,2124, 2126, 2142, 2146, 2148, etc.) involved in the product interactionbetween a shopper and service robot 2104 based on the one or moresignals received from the one or more sensors of service robot 2104.

In some embodiments, the method may include using the analysis of theone or more images and the requested supplemental information todetermine the association of the at least one pay-by-weight productinvolved with the selected product interaction with the particularshopper among the plurality of shoppers. As discussed above, one or moreproduct interactions may be ambiguous because, for example, processingdevice 202 may be unable to identify a shopper (e.g., 2112, 2114, 2116,etc.) and/or pay-by-weight product (e.g., 2122, 2124, 2126, 2142, 2146,2148, etc.) associated with a particular product interaction. In suchcases, processing device 202 may receive supplemental information fromstore associate 2102 as discussed above. Processing device 202 may beconfigured to use both the image analysis of the image data obtainedfrom the one or more sensors 310 and the supplemental informationprovided by store associate 2102 to identify the shopper and/or theproduct associated with a particular product interaction. Processingdevice 202 may also be configured to associate a shopper (e.g., 2114)with a pay by weight product (e.g., 2142) based on a combination of theanalysis of the one or more images and the supplemental informationprovided by store associate 2102.

In some embodiments, the association of the at least one pay-by-weightproduct involved with each product interaction with the particularshopper among the plurality of shoppers may be further based on scanningof a barcode associated with the at least one pay-by-weight product. Asdiscussed above, processing device 202 may identify a shopper (e.g.,2112, 2114, 2116, etc.) associated with a product interaction based onthe analysis of the one or more images obtained by image sensor 310and/or based on supplemental information provided by store associate2102. Processing device 202 may also be configured to associate apay-by-weight product (e.g., 2122, 2124, 2126, 2142, 2146, 2148, etc.)with an identified shopper (e.g., 2112, 2114, 2116, etc.) based oninformation associated with the pay-by-weight product. As one example,store associate 2102 and/or shopper (e.g., 2112, 2114, 2116, etc.) mayscan a barcode 2154 (see FIG. 21 ) attached to pay-by-weight product2148. Processing device may receive information associated withpay-by-weight product 2148 based on scanning of barcode 2154. In someembodiments, the association of the at least one pay-by-weight productinvolved with each product interaction with the particular shopper amongthe plurality of shoppers may be further based on interrogation of anRFID tag associated with the at least one pay-by-weight product. It iscontemplated that in some embodiments, store associate 2102 and/orshopper (e.g., 2112, 2114, 2116, etc.) may use, for example, an RFIDreader to read RFID tag 2156 associated with pay-by-weight product 2146.Processing device 202 may receive information associated withpay-by-weight product 2146 based on scanning (e.g., interrogation) ofRFID tag 2156. Processing device 202 may be configured to associatepay-by-weight product 2148 or 2146 with a shopper (e.g., 2112, 2114,2116, etc.) based on the information received from, for example,scanning of the barcode 2154 and/or scanning of the RFID tag 2156.

In some embodiments, the method may further comprise requesting from atleast one of the plurality of shoppers additional information to assistin the association of the at least one pay-by-weight product involvedwith the selected product interaction with the particular shopper amongthe plurality of shoppers. It is contemplated that in some embodimentsprocessing device 202 may request additional information from the one ormore shoppers (e.g., 2112, 2114, 2116, etc.) in addition to or as analternative to the supplemental information requested from storeassociate 2102. For example, processing device 202 may transmit anotification to a computing device associated with the one or moreshoppers (e.g., 2112, 2114, 2116, etc.), requesting them to provideadditional information regarding one or more product interactions. Thecomputing device associated with the one or more shoppers may includeone or more of a smartphone, a tablet computer a smartwatch, a mobilephone, a laptop computer, a smart glass, etc. The notification mayinclude requests for information such as, whether shopper (e.g., 2112,2114, 2116, etc.) placed an order for pay-by-weight product (e.g., 2122,2124, 2126, 2142, 2146, 2148, etc.), a quantity (e.g., volume or weight,etc.) of the pay-by-weight product that may have been ordered by ashopper, a brand of the pay-by-weight product that may have been orderedby a shopper, etc. The one or more shoppers (e.g., 2112, 2114, 2116,etc.) may provide the additional information in the form of one or moregestures that may be captured in the one or more images obtained byimage sensors 310. Processing device 202 may analyze the one or moreimages obtained by the one or more sensors 310 to identify the gesturesmade by the one or more shoppers (e.g., 2112, 2114, 2116, etc.).Processing device 202 may also be configured to identify thepay-by-weight product (e.g., 2122, 2124, 2126, 2142, 2146, 2148, etc.)that may be associated with a particular shopper based on the gesturesdetected in the one or more images.

It is also contemplated that in some embodiments, the one or moreshoppers (e.g., 2112, 2114, 2116, etc.) may provide the additionalinformation by selecting one or more icons or widgets, or by enteringtext on a computing device associated with the one or more shoppers(e.g., 2112, 2114, 2116, etc.). Processing device 202 may receivesignals from the computing device associated with the one or moreshoppers (e.g., 2112, 2114, 2116, etc.) and determine the additionalinformation based on the received signals. It is further contemplatedthat the one or more shoppers may provide the additional information tostore associate 2102, who in turn may provide the additional informationto processing device 202 in the form of one or more gestures, and/or byselecting one or more icons or widgets, and/or entering information oncomputing device 2210 associated with store associate 2102. Processingdevice 202 may detect the one or more gestures made by store associate2102 based on an analysis of the one or more images received by the oneor more image sensors 310. Processing device 202 may determine theadditional information based on the identified gestures made by storeassociate 202. Additionally or alternatively, processing device 202 mayreceive signals from computing device 2210 associated with storeassociate 2102 and determine the additional information based on thereceived signals.

In some embodiments, the additional information requested from the atleast one of the plurality of shoppers may be forgone when a credibilityindicator associated with the at least one of the plurality of shoppersis below a selected threshold. A retailer may determine that additionalinformation regarding a pay-by-weight product interaction may not benecessary from a shopper who may be deemed to be not credible ortrustworthy by the retailer. For example, a shopper's past shoppingbehavior may indicate whether the shopper is a trusted shopper (e.g.,whether the shopper is credible or trustworthy). A trusted shopper asused in this disclosure may be determined based on information in thecustomer profile that indicates, for example, that shopper (e.g., 2112,2114, 2116, etc.) has previously informed the retail store 105 regardingincorrect product interactions (e.g., when the shopper has received anincorrect pay-by-weight product). As another example, a trusted shoppermay be determined based on information in the customer profileindicating that (e.g., 2112, 2114, 2116, etc.) has previously informedretail store 105 regarding errors in the price of products previouslypurchased by the shopper (e.g., when the shopper has been charged anerroneously lower price for a product). Other criteria to determinewhether a shopper is a trusted shopper may include determining whetherthe shopper has paid for purchased products on time, and/or whether theshopper has a good credit history, etc. It is to be understood thatthese criteria for defining a trusted shopper are exemplary andnonlimiting and that many these or other criteria may be usedindividually or in any combination to define a trusted shopper.

Processing device 202 may be configured to determine a credibilityindicator associated with a shopper (e.g., 2112, 2114, 2116, etc.) basedon, for example, the customer profile discussed above. By way ofexample, processing device 202 may assign a relatively high credibilityindicator (e.g., greater than or equal to 75%) when the shopper (e.g.,2112, 2114, 2116, etc.) is deemed to be a trusted shopper. In someembodiments processing device 202 may assign a relatively highcredibility indicator (e.g., greater than or equal to 75%) when theshopper (e.g., 2112, 2114, 2116, etc.) is a returning customer, that is,when the information in a customer profile associated with shopper(e.g., 2112, 2114, 2116, etc.) indicates that the shopper has previouslyshopped at a particular retail store (e.g., 105C). In contrast,processing device 202 may assign a relatively lower credibilityindicator (e.g., 30% or less) to a shopper that is not a trusted shopperor for example to a first-time shopper. It is contemplated thatprocessing device 202 may forego transmitting notifications to a shopper(e.g., 2112, 2114, 2116, etc.), having a low credibility indicator. Forexample, a shopper having a low credibility indicator may suggest thatany additional information provided by that particular shopper may notbe trustworthy. Accordingly, processing device 202 may be configured toforego transmitting the notification, requesting additional information,to a shopper having a low credibility indicator.

In some embodiments, the at least one of the plurality of shoppers maybe selected from the plurality of shoppers based on a plurality ofcredibility indicators associated with the plurality of shoppers. Asdiscussed above, processing device 202 may be configured to assigncredibility indicators to the one or more shoppers (e.g., 2112, 2114,2116, etc.) who may be involved in one or more pay-by-weight productinteractions. Processing device 202 may select a shopper from theplurality of shoppers based on the credibility indicator, and mayprovide a notification to the selected shopper, requesting additionalinformation. For example, processing device 202 may have assignedshopper 2112 and shopper 2114 credibility indicators of 40% and 70%,respectively. Although both credibility indicators may be less than athreshold credibility indicator value (e.g., 90%), processing device 202may select shopper 2114 having a higher credibility indicator forproviding additional information that may help to associate apay-by-weight product (e.g., 2142) with that particular shopper 2114. Itis to be understood that the numerical values of 30%, 40%, 70% 75%, 90%etc., discussed above are exemplary and nonlimiting and othercredibility indicator values may be used. It is also to be understoodthat in some embodiments the credibility indicator may instead taketextual values (e.g., low, medium, high).

In some embodiments, the method may include updating a virtual shoppingcart of the particular shopper among the plurality of shoppers with theat least one pay-by-weight product involved with the selected productinteraction. As discussed above, processing device 202 may performanalysis of the image data obtained by the one or more image sensor 310,request supplemental information from store associate 2102, and/orrequest additional information from the one or more shoppers (e.g.,2112, 2114, 2116, etc.) to identify a shopper and/or a pay-by-weightproduct associated with a product interaction, and to associate theidentified shopper and the identified pay-by-weight product. Processingdevice 202 may also be configured to update a virtual shopping cartassociated with the identified shopper (e.g., 2112, 2114, 2116, etc.).For example, processing device 202 may be configured to add theidentified pay-by-weight product to a list of products purchased by theidentified shopper based on the analysis of the one or more images,supplemental information from source associate 2102, and/or additionalinformation obtained from one or more shoppers (e.g., 2112, 2114, 2116,etc.). Items in the virtual shopping cart may be used by processingdevice 202 to, for example, automatically withdraw payment from a debitor credit account of an associated shopper or, for example, to issue aninvoice an associated shopper for the purchased products.

FIG. 23 is a flowchart showing an exemplary process 2300 for updatingvirtual shopping carts of shoppers with pay-by-weight products. Process2300 may be performed by one or more processing devices associated withserver 135, such as processing device 202.

In step 2302, process 2300 may include receiving one or more imagescaptured by one or more image sensors, wherein the one or more imagesmay depict product interactions between a store associate and aplurality of shoppers, wherein each of the product interactions mayinvolve at least one pay-by-weight product. For example, as discussedabove, a retail store (e.g., 105A, 105B, 105C, etc., see FIG. 1 ) mayinclude one or more capturing devices 125 configured to capture one ormore images. One or more of capturing devices 125 may include one ormore image sensors 310 that may be configured to capture images of oneor more persons (e.g., shoppers, store associates, etc.), one or morepay-by-weight products (e.g., 2122, 2124, 2126, 2142, 2144, 2146, etc.),and/or other objects (e.g., shopping carts, checkout counters, walls,columns, poles, aisles, pathways between aisles), etc. The images may bein the form of image data, which may include, for example, pixel datastreams, digital images, digital video streams, data derived fromcaptured images, etc.

In step 2304, process 2300 may include analyzing the image data toidentify at least one shopper at one or more locations of the retailstore. For example, processing device 202 may analyze the image dataobtained by the one or more image sensors 310 to identify one or morepersons or objects in the image data. It is contemplated that processingdevice 202 may use any suitable image analysis technique, for example,including one or more of object recognition, object detection, imagesegmentation, feature extraction, optical character recognition (OCR),object-based image analysis, shape region techniques, edge detectiontechniques, pixel-based detection, artificial neural networks,convolutional neural networks, etc., to identify one or more persons orobjects in the image data. It is further contemplated that processingdevice 202 may access one or more databases 140 to retrieve one or morereference images of likenesses of one or more persons. Further,processing device 202 may use one or more of the image analysistechniques discussed above to compare the images retrieved from database140 with the image data received from the one or more image sensors 310to recognize the likeness of one or more shoppers (e.g., 2112, 2114,2116, etc.) or to recognize one or more pay-by-weight products (e.g.,2122, 2124, 2126, 2142, 2144, 2146, etc.) in the image data. It is alsocontemplated that processing device 202 may retrieve other identifyinginformation (e.g., name, customers ID, account number, telephone number,etc.) associated with the images retrieved from database 140 based on,for example, profiles of the one or more shoppers (e.g., 2112, 2114,2116, etc.) stored in database 140. It is further contemplated thatprocessing device 202 may retrieve information (e.g., product id, price,brand name, etc.) about the one or more pay-by-weight products.

Additionally or alternatively, in step 2304, process 2300 may includeanalyzing one or more images, such as the one or more images received bystep 2302, to identify the product interactions and/or to associate theat least one pay-by-weight product involved with each productinteraction with a particular shopper among the plurality of shoppers.In some examples, step 2304 may analyze the one or more images todetermine positions of hands of shoppers and of different pay-by-weightproducts. A particular product interaction, as well as a particularshopper and a particular pay-by-weight product corresponding to theproduct interaction, may be identified (among a plurality of shoppersand among a plurality of pay-by-weight products, respectively) based onproximity between the position of the hand of the particular shopper andthe particular pay-by-weight product. In some examples, a convolution ofat least a part of at least one image of the one or more images may becalculated, in response to a first value of the calculated convolution,an interaction of a first shopper with a particular pay-by-weightproduct may be identified, and in response to a second value of thecalculated convolution, an interaction of a second shopper with theparticular pay-by-weight product may be identified, the second shoppermay differ from the first shopper. In some examples, a convolution of atleast a part of at least one image of the one or more images may becalculated, in response to a first value of the calculated convolution,an interaction of a particular shopper with a first pay-by-weightproduct may be identified, and in response to a second value of thecalculated convolution, an interaction of the particular shopper with asecond pay-by-weight product may be identified, the second pay-by-weightproduct may differ from the first pay-by-weight product. In someexamples, the shopper may be identified by analyzing the one or moreimages using visual person detection algorithms. In some examples, theone or more images may be analyzed using a visual action recognitionalgorithm to determine whether the shopper interacts with thepay-by-weight product or only being (or appearing to be) in theproximity of the pay-by-weight product.

In step 2306, process 2300 may include providing a notification to thestore associate requesting supplemental information to assist in theassociation of the at least one pay-by-weight product involved with aselected product interaction with the particular shopper among theplurality of shoppers. For example, in some instances, processing device202 may not be able to identify either the shopper or the pay-by-weightproduct, or both because of the quality of the image data. For example,in some instances images obtained by the one or more sensors 310 may betoo dark because of insufficient light or the image may be too blurry.As a result, processing device 202 may not be able to identify shopper(e.g., 2114) and/or pay-by-weight product (e.g., 2142) being given toshopper 2114 by store associate 2102. As another example, portions of animage of shopper 2114 and/or pay-by-weight product 2142 may be occludedby another shopper (e.g., 2116), and/or another object. By way ofanother example, an image of product (e.g., 2122, 2124, 2126, 2142,2144, 2146, etc.) may be blurry or out of focus making it difficult toread a label on the product using optical character recognitiontechniques. As a result, processing device 202 may not be able toidentify shopper (e.g., 2112, 2114, 2116, etc.) and/or pay-by-weightproduct (e.g., 2122, 2124, 2126, 2142, 2144, 2146, etc.) based onanalysis of the images, and/or processing device 202 may not be able todetermine whether, for example, product 2142 was given to shopper 2114or 2116. In such situations, processing device 202 may provide anotification to store associate 2102, requesting supplementalinformation that may assist processing device 202 in identifying, forexample, which of shopper 2114 or 2116 may have received product 2142.Processing device 202 may also be able to use the supplementalinformation to associate product (e.g., 2142) with shopper (e.g., 2114).

In step 2308, process 2300 may include receiving the requestedsupplemental information from the store associate. For example, whenstore associate 2102 receives a notification from processing device 202,store associate 2102 may identify a shopper (e.g., 2112, 2114, 2116,etc.) and/or pay-by-weight product (e.g., 2122, 2124, 2126, 2142, 2146,2148, etc.). As discussed above, store associate 2102 may do so by usingone or more gestures and/or by making a selection on computing device2210 associated with store associate 2102. As one example, storeassociate 2102 may point to one of the shoppers (e.g., point to shopper2114 out of shoppers 2114 and 2116). Processing device 202 may detectthe pointing gesture based on an analysis of the one or more imagescaptured by image sensors 310. Processing device 202 may receive thesupplemental information in the form of the detected gesture. By way ofanother example, store associate 2102 may select one of the plurality ofimages of shoppers (e.g., 2202) displayed on computing device 2210associated with store associate 2102 to identify the shopper that mayhave received a pay-by-weight product. Processing device 202 may receivea signal from computing device 2210, identifying the shopper selected bystore associate 2102 (e.g., supplemental information).

In step 2310, process 2300 may include determining the association ofthe at least one pay-by-weight product involved with the selectedproduct interaction with the particular shopper among the plurality ofshoppers, using the analysis of the one or more images and the requestedsupplemental information. As discussed above, one or more productinteractions may be ambiguous because, for example, processing device202 may be unable to identify a shopper (e.g., 2112, 2114, 2116, etc.)and/or pay-by-weight product (e.g., 2122, 2124, 2126, 2142, 2146, 2148,etc.) associated with a particular product interaction. In such cases,processing device 202 may receive supplemental information from storeassociate 2102 as discussed above. Processing device 202 may beconfigured to use both the image analysis of the image data obtainedfrom the one or more sensors 310 and the supplemental informationprovided by store associate 2102 to identify the shopper and/or theproduct associated with a particular product interaction. Processingdevice 202 may also be configured to associate a shopper (e.g., 2114)with a pay by weight product (e.g., 2142) based on a combination of theanalysis of the one or more images and the supplemental informationprovided by store associate 2102. In some examples, the analysis of theone or more images by step 2304 may fail to identify the productinteractions and/or to associate the at least one pay-by-weight productinvolved with each product interaction with a particular shopper amongthe plurality of shoppers or may fail to do so at a sufficientconfidence level. The requested supplemental information may be used toovercome the failure to identify or to increase the confidence of theidentification to a sufficient confidence level. For example, thefailure may be due to an ambiguity among two or more shoppers based onthe analysis of the one or more image alone, and the requestedsupplemental information may be used to resolve the ambiguity and decideon a particular shopper of the two or more shoppers. In another example,the failure may be due to ambiguity among two or more pay-by-weightproducts based on the analysis of the one or more image alone, and therequested supplemental information may be used to resolve the ambiguityand decide on a particular pay-by-weight product of the two or morepay-by-weight products. In yet another example, the failure may be dueto inability to determine whether a particular product interactionoccurred or not based on the analysis of the one or more image alone,and the requested supplemental information may be used to determinewhether the particular product interaction occurred or not.

In step 2312, process 2300 may include updating a virtual shopping cartof the particular shopper among the plurality of shoppers with the atleast one pay-by-weight product involved with the selected productinteraction. As discussed above, processing device 202 may performanalysis of the image data obtained by the one or more image sensor 310,request supplemental information from store associate 2102, and/orrequest additional information from the one or more shoppers (e.g.,2112, 2114, 2116, etc.) to identify a shopper and/or a pay-by-weightproduct associated with a product interaction, and to associate theidentified shopper and the identified pay-by-weight product. Processingdevice 202 may also be configured to update a virtual shopping cartassociated with the identified shopper (e.g., 2112, 2114, 2116, etc.).For example, processing device 202 may be configured to add theidentified pay-by-weight product to a list of products purchased by theidentified shopper based on the analysis of the one or more images,supplemental information from source associate 2102, and/or additionalinformation obtained from one or more shoppers (e.g., 2112, 2114, 2116,etc.). Items in the virtual shopping cart may be used by processingdevice 202 to, for example, automatically withdraw payment from a debitor credit account of an associated shopper or, for example, to issue aninvoice an associated shopper for the purchased products.

Providing a frictionless experience when products are stored in bulkproduct packages (e.g., shelf-ready boxes or other packages) ischallenging. One source of difficulty is that cameras may be unable toadequately image regions within or near the bulk packages. As a result,a system solely relying on cameras to capture interactions between ashopper and products may have difficulty assessing how many items aparticular shopper removes from a particular bulk package. For example,a camera may have captured images showing that a shopper picked one ormore products from a bulk package disposed on a shelving unit. However,due to the relative position between the shopper, the bulk package, andthe camera, a part of the shopper may have blocked the bulk package inthe images captured by the camera, such that it may be difficult toassess the number of products picked by the shopper solely based on thecaptured images. For another example, while the captured images may havesufficient resolutions to enable the assessment of the number of largeproducts (e.g., a pack of toilet paper), the resolutions may beinsufficient for assessing the number of smaller products (e.g.,deodorant packages). As still another example, while some capturedimages may show that a shopper's hand moves closer to, or even touches,a product, it may be difficult to determine whether the shopper haspicked a product or placed a product back to the bulk package solelybased on the captured images. To address this issue, an embodiment ofthe present disclosure uses a combination of different types of sensors(e.g., cameras, weight sensors, pressure sensors, etc.) to facilitatefrictionless checkout of products that are stored in bulk packages. Forexample, images captured from a camera may be used to identify a bulkpackage with which the shopper interacts (which can enable determinationof a product type), and a weight sensor can be used to determine thenumber of products removed from the identified bulk packaging.

As noted generally above, a retail environment may provide africtionless checkout experience. As used herein, a frictionlesscheckout refers to any checkout process for a retail environment with atleast one aspect intended to expedite, simplify, or otherwise improve anexperience for customers. In some embodiments, a frictionless checkoutmay reduce or eliminate the need to take inventory of products beingpurchased by the customer at checkout. For example, this may includetracking the selection of products made by the shopper so that they arealready identified at the time of checkout. The tracking of products mayoccur through the implementation of sensors used to track movement ofthe shopper and/or products within the retail environment, as describedthroughout the present disclosure. Additionally or alternatively, africtionless checkout may include an expedited or simplified paymentprocedure. For example, if a retail store has access to paymentinformation associated with a shopper, the payment information may beused automatically or upon selection and/or confirmation of the paymentinformation by the user. In some embodiments, a frictionless checkoutmay involve some interaction between the user and a store associate orcheckout device or terminal. In other embodiments, the frictionlesscheckout may not involve any interaction. For example, the shopper maywalk out of the store with the selected products and a paymenttransaction may occur automatically. While the term “frictionless” isused for purposes of simplicity, it is to be understood that thisencompasses semi-frictionless checkouts as well. Accordingly, varioustypes of checkout experiences may be considered “frictionless,” and thepresent disclosure is not limited to any particular form or degree offrictionless checkout.

FIG. 24 is an illustration of an exemplary system 2400 for identifyingproducts removed from bulk packaging, consistent with some embodimentsof the present disclosure. As illustrated in FIG. 24 , system 2400 mayinclude a server 2401, a database 2402, a plurality of cameras 2403 anda plurality of sensors 2404 in a retail store 2410, a network 2405, anda personal device 2406A associated with a shopper 2406B in the retailstore 2410. Retail store 2410 may include one or more shelving units, onwhich bulk packages are disposed. Each bulk package may contain one ormore products.

In the embodiment illustrated in FIG. 24 , server 2401 may be acloud-based server that communicates with sensors 2404 and personaldevice 2406A via the network 2405. In some other embodiments, server2401 may be part of a system associated with the retail store 2401 thatcommunicates with sensors 2404 using a wireless local area network(WLAN).

Server 2401 may be coupled to one or more physical or virtual storagedevices such as database 2402. Database 2402 may store informationrelated to various products in retail store 2410, as well informationrelated to various shoppers in retail store 2410. The information may beaccessed by server 2401 to identify products and quantity changesrelated to the identified products.

Cameras 2403 may be disposed in various locations in retail store 2410to capture static or moving images of various locations in retail store2410. Cameras 2403 may transmit the captured images to server 2401 vianetwork 2405. Server 2401 may execute an image analysis process toidentify shoppers as well as products and/or bulk packages in thecaptured images, and interactions between the shoppers and the productsand/or bulk packages. For example, when server 2401 detects, based onthe captured images, that a distance between a shopper, or a part of theshopper (e.g., a hand), and a product or a bulk package is less than apredetermined threshold, server 2401 may determine that the shopper hasinteracted with the product or the bulk package. The interactionsbetween the shopper and the products or bulk packages may include, forexample, a shopper picking up a product from a bulk package and placingthe picked product 2530 in a shopping cart or basket, picking up aproduct from a bulk package and carrying the picked product away, orpicking up a product from a bulk package and then placing the pickedproduct back inside the bulk package, etc.

Sensors 2404 may comprise various types of sensors disposed in variouslocations in retail store 2410 for measuring one or more parameters ofthe products disposed in retail store 2410. For example, sensors 2404may comprise detection elements 801A and 801B described with referenceto FIGS. 8A and 8B.

In some embodiments, sensors 2404 may comprise one or more spatialsensors arranged to capture interactions between shopper 2406B and oneor more bulk packages each configured to contain a plurality ofproducts. For example, the spatial sensors may comprise light detectionand ranging (LIDAR) sensors, motion sensors, image sensors,radio-frequency identification (RFID) readers, piezoresistive sensors,light sensors, radio detection and ranging (RADAR) sensors, acousticsensors, and more.

In some embodiments, sensors 2404 may comprise one or more sensorsconfigured to monitor changes associated with the bulk packages, such asaddition or removal of one or more products in the bulk packages. Forexample, sensors 2404 may comprise a weight sensor (e.g., a weightsensitive pad) configured to monitor changes in a weight of a bulkpackage, or a pressure sensor.

Personal device 2406A may communicate with server 2401 to presentinformation derived by server 2401 based on processing of image dataacquired by cameras 2403 and sensing data acquired by sensors 2404. Forexample, personal device 2406A may present a virtual shopping cart,which may include a list of products that has been removed by shopper2406B from the store shelf and a number of these products. An example ofthe virtual shopping cart presented in personal device 2406A may beillustrated in FIG. 11E. Personal device 2406A may be all possible typesof devices capable of outputting a virtual shopping cart managed byserver 2401 to shopper 2406B, such as a mobile device, a tablet, apersonal digital assistant (PDA), etc.

FIG. 25A is a schematic illustration of an example configuration ofretail store 2500, consistent with the embodiments of the presentdisclosure. FIG. 25B is a schematic illustration of a front view of ashelving unit 2510 in retail store 2500, consistent with the embodimentsof the present disclosure.

As shown in FIGS. 25A and 25B, retail store 2500 may include shelvingunit 2510 on which a plurality of bulk packages 2520A, 2520B, 2520C,2520D, 2520E, 2520F (collectively referred to as “bulk packages 2520”)are disposed. Each of bulk packages 2520 may respectively contain aplurality of products 2530A, 2530B, 2530C, 2530D, 2530E, 2530F(collectively referred to as “products 2530”). In some embodiments, bulkpackages 2520 may include shelf-ready packages configured to hold aplurality of products 2530 each weighing less than 1 kg.

Retail store 2500 may also include one or more camera 2550 configured tocapture image data, and one or more sensors 2560A, 2560B, 2560C, 2560D(collectively referred to as “sensors 2560”) configured to monitorchanges associated with bulk packages 2520. Camera 2550 and sensors 2560may be transmit captured images and sensing data to a server (such asserver 2401 illustrated in FIG. 24 ) via a network (such as network 2405illustrated in FIG. 24 ).

Camera 2550 may be arranged to capture images showing interactionsbetween a shopper 2580 and one or more bulk packages 2520. For example,as shown in FIG. 25A, camera 2550 may capture an image showing aninteraction between shopper 2580 and bulk package 2520A arranged on aleft side of an upper shelf of shelving unit 2510. The interaction shownin the captured image may indicate that a hand of shopper 2580 isadjacent to, or touches, bulk package 2520A or one or more products2530A contained in bulk package 2520A. In some embodiments, the imagealone may be insufficient for determining whether products were removedfrom the particular bulk package 2520A, or the quantity of productsremoved from bulk package 2520A.

Sensors 2560 may be configured to monitor changes associated with theone or more bulk packages 2520. In some embodiments, sensors 2560 mayinclude one or more weight sensors configured to monitor changes in theweight of bulk packages 2520. For example, as shown in FIGS. 25A and25B, weight sensor 2560A is disposed under bulk packages 2520A, 2520B,and 2520C to monitor changes in a weight of a combination of bulkpackages 2520A, 2520B, and 2520C. Weight sensors 2560B, 2560C, and 2560Dare respectively disposed under bulk packages 2520D, 2520E, and 2520F tomonitor respective weight changes in bulk packages 2520D, 2520E, and2520F. In some other embodiments, sensors 2560 may include other typesof sensors. For example, sensors 2560 may include one or more pressuresensors configured to be located such that one or more bulk packages2520 may apply detectable pressure on the pressure sensors.

In some embodiments, the output from sensor 2560 alone may beinsufficient for determining whether products were removed from aparticular bulk package. For example, as shown in FIG. 25B, a product2530A in bulk package 2520A and a product 2530C in bulk package 2530Amay have the same weight. Therefore, when a product is removed from bulkpackage 2520A or bulk package 2520C, the output from weight sensor 2560Amay indicate that the combined weight of bulk packages 2520A, 2520B, and2520C is reduced by one product 2530A or 2530C. However, the output fromweight sensor 2560A may be insufficient for determining whether theproduct is removed from bulk package 2520A or bulk package 2520C. Thus,according to embodiments of the present disclosure, the output fromdifferent types of sensors may be used to determine the types andquantities of products being removed, thus facilitating frictionlesscheckout.

FIG. 26A includes a flowchart representing an exemplary method 2600 foridentifying products removed from bulk packaging, consistent with anembodiment of the present disclosure. Method 2600 may be performed by aprocessor at a server (e.g., server 135 or 2401) or a computer (e.g.,one of devices 145A, 145B, 145C, and 145D). For purposes ofillustration, in the following description, reference is made to certaincomponents of images shown in FIGS. 24, 25A, and 25B. It will beappreciated, however, that other implementations are possible and thatother configurations may be used to implement method 2600. It will alsobe readily appreciated that the illustrated method can be altered tomodify the order of steps, delete steps, or further include additionalsteps.

For example, method 2600 may be performed by at least one processingdevice of a server, such as processing device 302, as described above.In some embodiments, some or all of method 2600 may be performed by adifferent device associated with system 100. In some embodiments, anon-transitory computer readable medium may contain instructions thatwhen executed by a processor cause the processor to perform method 2600.Further, method 2600 is not necessarily limited to the steps shown inFIG. 26A, and any steps or processes of the various embodimentsdescribed throughout the present disclosure may also be included inmethod 2600.

At step 2612, the processor at the server or the computer may receiveone or more images acquired by a camera arranged to capture interactionsbetween a shopper and one or more bulk packages each configured tocontain a plurality of products. For example, as shown in FIG. 25A, theprocessor may receive one or more images acquired by camera 2550arranged to capture interactions between shopper 2580 and bulk packages2520 disposed on shelving unit 2510. Each bulk package 2520 may includea plurality of products 2530. The interactions between shopper 2580 andbulk packages 2520 may include, for example, shopper 2580 picking up aproduct 2530 from a bulk package 2520 and placing the picked product2530 in a shopping cart or basket, picking up a product 2530 from a bulkpackage 2520 and carrying the picked product 2530 away from shelvingunit 2510, or picking up a product 2530 from a bulk package 2520 andthen placing the picked product 2530 back inside bulk package 2520, etc.

At step 2614, the processor may analyze the one or more images toidentify the shopper and a particular bulk package among the one or morebulk packages with which the identified shopper interacted. For example,as shown in FIG. 25A, the processor may analyze one or more imagesacquired by camera 2550. As a result of the analysis, the processor maydetermine an identity of shopper 2580 and bulk package 2520A with whichshopper 2580 interacted. In the embodiment illustrated in FIG. 26 , theone or more images alone may be insufficient for the processor todetermine whether products 2530A were removed from bulk package 2520A.In some examples, step 2614 may analyze the one or more images todetermine positions of a hand of customer 2580 and of the different bulkpackages 2520, and the particular bulk package may be identified amongthe one or more bulk packages based on proximity between the position ofthe hand of customer 2580 and the particular bulk package. In someexamples, a convolution of at least a part of at least one image of theone or more images may be calculated, in response to a first value ofthe calculated convolution, an interaction of the shopper with a firstbulk package among the one or more bulk packages may be identified, andin response to a second value of the calculated convolution, aninteraction of the shopper with a second bulk package among the one ormore bulk packages may be identified, the second bulk package may differfrom the first bulk package. In some examples, the shopper may beidentified by analyzing the one or more images using visual persondetection algorithms. In some examples, the one or more images may beanalyzed using a visual action recognition algorithm to determinewhether the shopper interacts with the particular bulk package or onlybeing (or appearing to be) in the proximity of the particular bulkpackage.

In some embodiments, the processor may analyze the one or more imagescaptured by the camera to determine a product type associated with theparticular bulk package. For example, the processor may determine theproduct type from a logo displayed on the particular bulk package, fromtextual information displayed on the particular bulk package, and from alabel disposed on the shelving unit next to the particular bulk package.In some examples, OCR algorithms may be used to analyze the one or moreimages and identify text appearing on the particular bulk package, andthe determination of the product type associated with the particularbulk package may be based on the identified text.

At step 2616, the processor may receive an output from at least onesensor configured to monitor changes associated with the particular bulkpackage. For example, as shown in FIG. 25A, the processor may receive anoutput from sensor 2560A which is configured to monitor changesassociated with bulk package 2520A. Sensor 2560A may include a weightsensor configured to monitor changes in a weight of bulk package 2520A.

At step 2618, the processor may analyze the output from the at least onesensor to determine a quantity of products removed from the particularbulk package by the identified shopper. The at least one sensor mayinclude a pressure sensor or a weight sensor. The at least one sensormay be disposed on a retail shelf and may be disposed under theparticular bulk package. For example, as shown in FIGS. 25A and 25B, theprocessor may analyze the output from weight sensor 2560A. As a resultof the analysis, the processor may determine a quantity of products2530A removed from bulk package 2520A by shopper 2580. In the embodimentillustrated in FIGS. 25A and 25B, because weight sensor 2560 extendsunder all of bulk packages 2520A, 2520B, and 2525C and thus monitorschanges of a combined weight of bulk packages 2520A, 2520B, and 2525C,the output from weight sensor 2560A alone may be insufficient for theprocessor to determine whether products 2530A were removed from bulkpackage 2520A, 2520B, or 2520C or from another bulk package among theone or more bulk packages. Additionally, in the embodiment illustratedin FIGS. 25A and 25B, the output from sensor 2560A alone is insufficientfor determining an identification of shopper 2580 that removes a product2530A from bulk package 2520A.

In some embodiments, the processor may obtain, from a database (e.g.,database 2402) information indicative of a first weight of a singleproduct associated with a first bulk package among the one or more bulkpackages and a second weight of a single product associated with asecond bulk package among the one or more bulk packages. Then, theprocessor may determine a quantity of products removed from theparticular bulk package based on, at least in part, on the first weightand the second weight. For example, the processor may obtain a firstweight of a single product 2530A associated with bulk package 2520A, anda second weight of a single product 2530B associated with bulk package2530B. Then, the processor may determine a quantity of products 2530Aremoved from bulk package 2520A based on, at least in part, on the firstweight and the second weight.

At step 2620, the processor may update a virtual shopping cartassociated with the identified shopper to include the determinedquantity of products and an indication of a product type associated withthe particular bulk package. For example, as shown in FIG. 25A, theprocessor may update a virtual shopping cart associated with shopper2580 to include the determined quantity of products 2530A and anindication of a product type associated with bulk package 2520A.

In some embodiment, if the quantity of products removed from theparticular bulk package cannot be conclusively determined by theprocessor, the processor may deliver a notification to the identifiedshopper that the identified shopper is not eligible for frictionlesscheckout. For example, the processor may deliver a notification to apersonal device associated with the identified shopper.

In the embodiment illustrated in FIG. 26A, camera 2550 may be used tocapture interactions between a shopper and one or more bulk packages. Insome alternative embodiments, one or more spatial sensors may be used tocapture interactions between a shopper and one or more bulk packages.Such an embodiment may be implemented according to FIG. 26B.

FIG. 26B includes a flowchart representing an exemplary method 2650 foridentifying products removed from bulk packaging, consistent withanother embodiment of the present disclosure. Similar to method 2600,method 2650 may also be performed by a processor at a server (e.g.,server 135 or 2401) or a computer (e.g., one of devices 145A, 145B,145C, and 145D). It will be readily appreciated that the illustratedmethod can be altered to modify the order of steps, delete steps, orfurther include additional steps.

For example, method 2650 may be performed by at least one processingdevice of a server, such as processing device 302, as described above.In some embodiments, some or all of method 2650 may be performed by adifferent device associated with system 100. In some embodiments, anon-transitory computer readable medium may contain instructions thatwhen executed by a processor cause the processor to perform method 2650.Further, method 2650 is not necessarily limited to the steps shown inFIG. 26B, and any steps or processes of the various embodimentsdescribed throughout the present disclosure may also be included inmethod 2650.

At step 2662, the processor at the server or the computer may receive anoutput from one or more spatial sensors arranged to capture interactionsbetween a shopper and one or more bulk packages each configured tocontain a plurality of products. The one or more sensors may include atleast one of a LIDAR system, a motion sensor, a camera, an RFID reader,or a piezoresistive sensor.

At step 2664, the processor may analyze the output from the one or morespatial sensors to identify the shopper and a particular bulk packageamong the one or more bulk packages with which the identified shopperinteracted.

At step 2666, the processor may receive an output from at least oneadditional sensor configured to monitor changes associated with theparticular bulk package. The at least one additional sensor may includea weight sensor configured to monitor changes in a weight of theparticular bulk package. Alternatively or additionally, the at least oneadditional sensor includes a pressure sensor.

At step 2668, the processor may analyze the output from the at least oneadditional sensor to determine a quantity of products removed from theparticular bulk package by the identified shopper.

At step 2670, the processor may update a virtual shopping cartassociated with the identified shopper to include the determinedquantity of products and an indication of a product type associated withthe particular bulk package.

In an ideal frictionless shopping experience, shoppers receive immediatefeedback on their actions and the status of their shopping carts. Forexample, after detecting a product interaction (e.g., picking/returninga product from/to the shelf), a frictionless shopping system may informthe shopper that the system registered the detected product interaction.However, this real-time or near real-time feedback regarding productinteractions might be used by malicious shoppers to identify blind spotsof the frictionless shopping system in the retail store as part of ashoplifting strategy. In one aspect of this disclosure, the suggestedfrictionless system may provide less information to suspected shoppers.Specifically, system 100 may control how and what information isprovided to shoppers. In another aspect of this disclosure, thesuggested frictionless system may provide information at a slower rate.Specifically, system 100 may control when the information is provided toshoppers. Implementing these measures may help reduce the risk ofmalicious shoppers using the information provided by the system toidentify and take advantage of blind spots of the frictionless shoppingsystem.

FIG. 27 illustrates a schematic diagram of a top view of a first shopper2700A and a second shopper 2700B (collectively referred to as shoppers2700) walking in aisle 2702 of retail store 105. As described above withreference to FIG. 6A, a plurality of image sensors (e.g., image sensors2704A and 2704B) may be deployed in aisle 2702 as part of a frictionlessshopping system (e.g., system 100) for acquiring images to identify aplurality of product interaction events for shoppers 2700. In theillustrated example, the coverage of image sensors 2704A and 2704B maybe partial due to error during installment, due to a temporaryocclusion, or any other reason. Accordingly, the frictionless shoppingsystem includes a first blind spot 2706 and a second blind spot 2708 inaisle 2702. Picking products from these spots may not be detected by thefrictionless shopping system and may be used by malicious shoppers toexploit the system and steal from retail store 105.

Consistent with the present disclosure, shoppers 2700 may be providedwith frictionless shopping data. The term “frictionless shopping data”or simply “shopping data” refers to any information provided to shoppersand intended to expedite, simplify, or otherwise improve the checkoutprocess of shoppers. In some embodiments, the frictionless shopping datamay indicate one or more product interactions identified by thefrictionless shopping system. In other embodiments, shopping data mayindicate the need to take inventory of products being purchased by thecustomer at checkout associated, e.g., the eligibility statuses ofshoppers 2700. In yet other embodiments, the shopping data may beindicative of products currently included in virtual shopping cartsassociated with shoppers 2700. For example, the shopping data mayinclude an identification of a type of product and quantity associatedwith a detected product interaction event. Additional examples of theshopping data provided to shoppers 2700 are described throughout thedisclosure. Shoppers 2700 may be provided with the frictionless shoppingdata via their associated communication devices 2710 (e.g., acommunication device 2710A and a communication device 2710B). In theillustrated example, communication device 2710A of first shopper 2700Ais a personal communication device (e.g., smartphone). One skilled inthe art would recognize that the personal communication device mayinclude any wearable device (e.g., smart glasses, a smartwatch, aclip-on camera). Communication device 2710B of second shopper 2700B maybe a smart cart with a display. The smart cart may communicate with apersonal communication device of second shopper 2700B to update avirtual shopping cart associated with second shopper 2700B.

In an embodiment, the shopping data may be delivered to shoppers 2700 aspush notifications. The push notifications may be issued after adetermined number of product interactions have been detected, afterdetection of a trigger event (e.g., a detection that a shopper hasentered a checkout area of retail store 105, or issued periodicallywhile shoppers 2700 are in retail store 105 (e.g., every 1 minute, 2minutes, 5 minutes, 10 minutes etc.). In another embodiment, theshopping data may be delivered to shoppers 2700 by way of updating anonline interface accessible to shopper 2700 such at the online interfaceis descriptive of the status of the shopper's shopping cart. The onlineinterface may include a webpage or an application associated with retailstore 105. For example, a shopper may access his or her online shoppinglist and see it automatically being updated while collecting theproducts from shelves of retail store 105. In some cases, the virtualshopping cart of a shopper may be updated as the shopper selectsproducts from a retail shelf (adding a product to the list) and as theshopper returns products to the retail shelf (removing the product fromthe list).

FIG. 28 illustrates an exemplary embodiment of a memory device 2800containing software modules consistent with the present disclosure. Inparticular, as shown, memory device 2800 may include a sensorscommunication module 2802, a captured data analysis module 2804, ashopping data determination module 2806, a shoplift risk determinationmodule 2808, a detail level determination module 2810, an update ratedetermination module 2812, a shopper communication module, and adatabase access module 2816 in communication with database 2818. Modules2802 to 2816 may contain software instructions for execution by at leastone processor (e.g., processing device 202) associated with system 100.Sensors communication module 2802, captured data analysis module 2804,shopping data determination module 2806, shoplift risk determinationmodule 2808, detail level determination module 2810, update ratedetermination module 2812, shopper communication module 2814, anddatabase access module 2816 may cooperate to perform various operations.For example, sensors communication module 2802 may receive an data fromone or more sensors in retail store 105, captured data analysis module2804 may use the received data to detect a shopper and to identify aplurality of product interaction events for the detected shopper,shopping data determination module 2806 may determine frictionlessshopping data for the shopper, shoplift risk determination module 2808may use database 2818 to determine a likelihood that the shopper will beinvolved in shoplifting, detail level determination module 2810 maydetermine the detail level of frictionless shopping data to provide tothe shopper based on the determined shoplift risk, update ratedetermination module 2812 may determine update rate for updating theshopper with the shopping data also based on the determined shopliftrisk, and shopper communication module 2814 may cause a delivery of thedetermined shopping data to the shopper.

According to disclosed embodiments, memory device 2800 may be part ofsystem 100, for example, memory device 226. Alternatively, memory device2800 may be stored in an external database or an external storagecommunicatively coupled with server 135, such as one or more databasesor memories accessible over communication network 150. Further, in otherembodiments, the components of memory device 2800 and the varioussoftware modules may be distributed in more than one server and morethan one memory device.

In some embodiments, sensors communication module 2802 may receiveinformation from sensors 2801, located in retail store 105. In oneexample, sensors communication module 2802 may receive image data (e.g.,images or video) captured by a plurality of image sensors fixedlymounted in retail store 105 or derived from images captured by aplurality of image sensors fixedly mounted in retail store 105. Inanother example, sensors communication module 2802 may receive imagedata (e.g., images or data derived from images) from robotic capturingdevices configured to navigate autonomously within retail store 105 andto capture images of multiple types of products. In yet another example,sensors communication module 2802 may additionally receive data from oneor more shelf sensors disposed on a surface of the retail shelfconfigured to hold one or more products placed on the retail shelf. Theone or more shelf sensors may include any combination of pressuresensitive pads, touch-sensitive sensors, light detectors, weightsensors, light sensors, resistive sensors, ultrasonic sensors, and more.

In some embodiments, captured data analysis module 2804 may process theinformation collected by sensors communication module 2802 to detect ashopper and to identify a plurality of product interaction events forthe detected shopper in retail store 105. Consistent with the presentdisclosure, the plurality of product interaction events may involve theshopper taking a product from a shelf or returning a product to a shelf.In one embodiment, captured data analysis module 2804 may identify theplurality of product interaction events solely based on image data, forexample, image data received from a plurality of image sensors fixedlymounted in retail store 105 (e.g., as illustrated in FIG. 27 ). Inanother embodiment, captured data analysis module 2804 may identify theplurality of product interaction events using a combination of imagedata and data from one or more shelf sensors. For example, captured dataanalysis module 2804 may analyze the data received from detectionelements attached to store shelves, alone or in combination with imagescaptured in retail store 105 to detect a product interaction event.

Shopping data determination module 2806 may determine frictionlessshopping data for one or more shoppers. Consistent with the presentdisclosure, shopping data determination module 2806 may use artificialneural networks, convolutional neural networks, machine learning models,image regression models, and other processing techniques to determinethe frictionless shopping data. For example, captured data analysismodule 2806 may calculate a convolution of at least part of the imagedata. In response to a first value of the calculated convolution,shopping data determination module 2806 may determine a firstfrictionless shopping data for the product interaction event and, inresponse to a second value of the calculated convolution, shopping datadetermination module 2806 may determine a second frictionless shoppingdata for the same product interaction event. The second frictionlessshopping data may differ from the first frictionless shopping data. Insome embodiments, the shopping data may be indicative of productsinvolve with the product interactions identified by captured dataanalysis module 2804. For example, the first frictionless shopping datamay be indicative of an item added to a virtual shopping cart associatedwith the shopper, and the second frictionless shopping data may beindicative of a removal of an item from the virtual shopping cartassociated with the shopper. In other embodiments, the shopping data maybe indicative of the frictionless checkout eligibility status of ashopper. For example, the first frictionless shopping data may indicatethat the shopper is eligible for frictionless checkout and the secondfrictionless shopping data may indicate that the shopper is ineligiblefor frictionless checkout.

Shoplift risk determination module 2808 may determine the likelihoodthat a certain shopper will be involved in shoplifting. Consistent withthe present disclosure, determining the likelihood may includedetermining a shoplift risk level. The term “shoplift risk level” refersto any indication, numeric or otherwise, of a level (e.g., within apredetermined range) indicative of a probability that a given shopperwill attempt to shoplift. For example, the shoplift risk level may havea value between 1 and 10. Alternatively, the shoplift risk level may beexpressed as a percentage or any other numerical or non-numericalindication. In some cases, the system may compare the shoplift risklevel to a threshold. As mentioned above, the term “threshold” as usedherein denotes a reference value, a level, a point, or a range ofvalues. In operation, when a shoplift risk level associated with ashopper exceeds a threshold (or below it, depending on a particular usecase), the system may follow a first course of action and, when theshoplift risk level is below it (or above it, depending on a particularuse case), the system may follow a second course of action. The value ofthe threshold may be predetermined for all shoppers or may bedynamically selected based on different considerations. For example,when the shoplift risk level of a certain shopper exceeds a riskthreshold, the system may use detail level determination module 2810 andupdate rate determination module 2812 to reduce the shopping dataprovided to the certain shopper.

Consistent with the present disclosure, shoplift risk determinationmodule 2808 may determine the likelihood that the shopper will beinvolved in shoplifting using one or a combination of the followingmodules: an action identification module 2820, a shopper recognitionmodule 2822, a shopper trait estimation module 2824, and a shoppinghistory determination module 2826. Modules 2820-2826 may be part ofshoplift risk determination module 2808 or separate from shoplift riskdetermination module 2808. In some embodiments, the determined shopliftrisk level may be an aggregation (e.g., a weighted combination) of twoor more modules. For example, shoplift risk determination module 2808may make the determination of the shoplift risk level based on aweighted average shoplift risk level determined by at least some ofmodules 2820-2826. Different analyses may be assigned different weightsto different modules, and the disclosed embodiments are not limited toany particular combination of analyses and weights.

Action identification module 2820 is configured to use image data fromsensors 2801 to determine the likelihood that the shopper will beinvolved in shoplifting. Specifically, image analysis modules 2820 maydetect one or more actions taken by the shopper that may be classifiedas suspicious and determine a corresponding shoplift risk level. In thecontext of this disclosure, a suspicious action includes any action thatmay indicate the intent or the act of theft by a shopper. The suspiciousaction may include any one or more of, for example, a furtive glance bythe shopper, a shopper attempt to hide his/her face, a shopper attemptto hide a picked item, and the like. In one embodiment, image analysismodules 2820 may detect an avoidance action taken by the shopper toavoid at least one store associate. The detection of the avoidanceaction may result in a higher determined shoplift risk level than wherethe shopper is not detected as engaging in such avoidance action.

Shopper recognition module 2822 is configured to determine whether theshopper is a recognized shopper, e.g., a returned customer. In somecases, the determination that the shopper is a recognized shopper mayresult in a first determined shoplift risk level lower than a seconddetermined shoplift risk level resulting from a determination that theshopper is not a recognized shopper. In a first embodiment, thedetermination of whether the shopper is a recognized shopper may bebased on analysis of the received image data (e.g., from sensors 2801).For example, the determination of whether the shopper is a recognizedshopper may be based on a comparison of at least a portion of thereceived image data to image information stored in a recognized shoppersdatabase associated with retail store 105. In a second embodiment, thedetermination of whether the shopper is a recognized shopper may bebased on an interaction with an electronic device associated with theshopper. For example, the electronic device may be an RFID tag (e.g., atag associated with retail store 105) or a mobile communication device(e.g., communication device 2710A). In other examples, the electronicdevice may be included on a shopping basket associated with the shopper,and wherein the electronic device is configured to receive a shopperidentification code from the shopper.

Shopper trait estimation module 2824 is configured to estimate a traitof a shopper based on the analysis of the image data, and to determinethe likelihood that the shopper will be involved in shoplifting based,at least in part, on the estimated trait of the shopper. The estimatedtraits may include age, gender, income class, and more. For example,under the assumption that elderly shoppers may be less likely to try toshoplift than young shoppers, shopper trait estimation module 2824 mayassign, for example, higher risk level to shoppers between certain ages(e.g., between 16 and 28 years of age) than to shoppers between otherages (e.g., between 65 and 82 years of age). Specifically, in oneembodiment, a first estimated age may associated with a first determinedshoplift risk level that is lower than a second determined shoplift risklevel associated with a second estimated age, wherein the firstestimated age is greater than the second estimated age.

Shopping history determination module 2826 is configured retrieve ashopping history associated with a particular shopper from a shoppinghistory database (e.g., part of databases 2818), and to determine thelikelihood that the shopper will be involved in shoplifting based, atleast in part, on the retrieved shopping history. In one example, theshopping history retrieved from the database may include previousquestionable conduct by the shopper that may result in the determinationof a higher risk of shoplifting. In one embodiment, the shopping historydatabase may store facial signatures of shoppers that previously visitedretail store 105. The facial signatures may be used in identifying theshopper via the analysis of the image data and in retrieving theshopping history for the shopper from the shopping history database. Inadditional embodiments, the shopping history database may store ahistory records of returns made to the retail store by differentshoppers. Thereafter, shopping history determination module 2826 may usethe history of returns in determining the likelihood that a shopper willbe involved in shoplifting.

Using the determined shoplift risk level determined by shoplift riskdetermination module 2808, detail level determination module 2810 maydetermine the detail level of frictionless shopping data to provide tothe shopper. Consistent with the present disclosure, fewer details maybe provided in the frictionless shopping data to shoppers with a highershoplift risk level. Examples of different detail levels are describedbelow with reference to FIG. 29 . Independently from (or in coordinationwith) detail level determination module 2810, update rate determinationmodule 2812 may use the determined shoplift risk level determined byshoplift risk determination module 2808 to determine update rate forupdating the shopper with the frictionless shopping data. Consistentwith the present disclosure, a lower update rate for providing in thefrictionless shopping data may be used for shoppers with a highershoplift risk level. Examples of different update rates are describedbelow with reference to FIG. 30 .

Shopper communication module 2814 may cause delivery of the shoppingdata determined by shopping data determination module 2806 to theshopper at a detail level determined by detail level determinationmodule 2810 and/or at an updated rate determined by update ratedetermination module 2812. Consistent with the present disclosure,shopper communication module 2814 may include software instructions forfacilitating communications between the device on which it isimplemented (e.g., server 135) and mobile device 2815 associated withthe shopper. Shopper communication module 2814 may enable receipt andtransmission of data from and to one or more shoppers. For example, thereceived data may include a request for additional shopping data, andthe transmitted data may include the frictionless shopping data. In oneembodiment, mobile device 2815 may be a communication device thatbelongs to the shopper, for example, communication device 2710A. Inanother embodiment, mobile device 2815 may be a communication devicethat belongs to retail store 105 and provided to the shopper forconducting frictionless shopping, for example, communication device2710B may be part of a smart cart with an output display that providesthe determined shopping data.

In some embodiments, database access module 2816 may cooperate withdatabase 2818 to retrieve stored data. The retrieved data may include,for example, sales data, theft data (e.g., a likelihood that a certainproduct may be subject to shoplifting), shoppers identifying data,personal shopping history data, a schedule of arrivals of additionalproducts, inventory records, checkout data, calendar data, historicalproduct turnover data, and more. As described above, shoplift riskdetermination module 2808 may use the data stored in database 2818 todetermine a shoplift risk level for one or more shoppers. Database 2818may include separate databases, including, for example, a vectordatabase, raster database, tile database, viewport database, and/or auser input database, configured to store data. The data stored indatabase 2818 may be received from modules 2802-2814, server 135, fromany communication device associated with retail stores 105, marketresearch entity 110, suppliers 115, users 120, and more. Moreover, thedata stored in database 2818 may be provided as input using data entry,data transfer, or data uploading.

Modules 2802-2816 and 2820-2826 may be implemented in software,hardware, firmware, a mix of any of those, or the like. For example, ifthe modules are implemented in software, the modules may be stored in aserver (e.g., server 135) or distributed over a plurality of servers. Insome embodiments, any one or more of modules 2802-2816, modules2820-2826, and data associated with database 2818 may be stored indatabase 140 and/or located on server 135, which may include one or moreprocessing devices. Processing devices of server 135 may be configuredto execute the instructions of modules 2802-2816 and 2820-2826. In someembodiments, aspects of modules 2802-2816 and 2820-2826 may includesoftware, hardware, or firmware instructions (or a combination thereof)executable by one or more processors, alone, or in various combinationswith each other. For example, modules 2802-2816 and 2820-2826 may beconfigured to interact with each other and/or other modules of server135 to perform functions consistent with disclosed embodiments.

FIG. 29 depicts a table 2900 that describes two detail levels ofshopping data delivered to shoppers in four use cases 2902-2908. In theillustrated example, first shoplift risk level 2910 associated with afirst shopper is lower than second shoplift risk level 2912 associatedwith a second shopper. In other words, the determined likelihood thatthe second shopper will be involved in shoplifting is greater than thelikelihood that the first shopper will be involved in shoplifting.Moreover, as described above, the system may compare the determinedshoplift risk level to a threshold. In the example use cases, firstshoplift risk level 2910 may be below the shoplift risk threshold andsecond shoplift risk level 2920 may be greater than the shoplift riskthreshold.

In first use case 2902, the frictionless shopping data provided inconjunction with first shoplift risk level 2910 identifies both aquantity and a type of a particular product selected by the firstshopper, and the frictionless shopping data provided in conjunction withsecond shoplift risk level 2912 identifies a type, but not a quantity,of a particular product selected by the second shopper. In this context,the quantity of the particular product may include a number of items, anoverall weight, a size of the item, etc. For example, frictionlessshopping system (e.g., system 100) may deliver to the first shopper anindication that two 500 g packages of pasta were added to his or hervirtual cart. But the indication delivered to the second shopper onlyindicates that pasta was added to his or her virtual cart, withoutinforming the second shopper how many packages of pasta were added.

In second use case 2904, the frictionless shopping data provided inconjunction with first shoplift risk level 2910 identifies a type of aparticular product selected by the first shopper, and the frictionlessshopping data provided in conjunction with second shoplift risk level2912 identifies a general class, but not a specific type, of aparticular product selected by the second shopper. In this context, thetype of the particular product may include a brand name, a type ofcontainer (e.g., bottle or can), identifying detail (e.g., diet ornon-diet, type of flavor, etc.), and more. For example, the system maydeliver to the first shopper an indication that a bottle of Diet Cokewas added to his or her virtual cart. But the indication delivered tothe second shopper only indicates that a soft drink was added to his orher virtual cart, without informing the second shopper which type ofsoft drink was added.

In third use case 2906, the frictionless shopping data provided inconjunction with first shoplift risk level 2910 identifies productinteraction events in which products were removed from a retail shelfand also identifies product interaction events in which products werereturned to a retail shelf, and the frictionless shopping data providedin conjunction with second shoplift risk level 2912 identifies productinteraction events in which products were removed from a retail shelfbut does not identify product interaction events in which products werereturned to a retail shelf. For example, the system may deliver to thefirst shopper an indication that a bottle Diet Coke 20 fl. oz was addedto his virtual cart and that the regular Coke was deleted from hisvirtual cart. But the indication delivered to the second shopper onlyindicates that a bottle Diet Coca Cola was added to her virtual cart,without identifying that the regular Coca Cola was deleted from hervirtual cart.

In fourth use case 2908, the frictionless shopping data provided inconjunction with first shoplift risk level 2910 includes a notificationsent to the first shopper in response to each detected productinteraction event involving the first shopper, and the frictionlessshopping data provided in conjunction with second shoplift risk level2912 includes notifications regarding detected product interactionevents that are sent to the second shopper in response to a secondarytrigger event, rather than in response to detection of individualproduct interaction events. In some embodiments, the secondary triggerevent may include a detection that the second shopper has exited anaisle of the retail store. In other embodiments, the secondary triggerevent may include a detection that the second shopper has entered acheckout area of the retail store. In other embodiments, the secondarytrigger event may include expiration of a predetermined time period,reports including shopping data may be issued periodically while thesecond shopper is in the retail store, for example, every 1 minutes, 2minutes, 5 minutes, 10 minutes, etc.

The foregoing use cases provide examples of the kinds of details in theshopping data delivered to a shopper associated with a first determinedshoplift risk level than to a shopper associated with a seconddetermined shoplift risk level, wherein the second determined shopliftrisk level is higher than the first determined shoplift risk level. Aperson skilled in the art would recognize that these use cases are onlyexamples, and the disclosed system can be used to provide differentlevels of details.

FIG. 30 is a diagram 3000 showing example timelines illustrating twodifferent update rates for providing shopping data to shoppers,according to disclosed embodiments. In the illustrated diagram, firsttimeline 3002 represents the product interaction events and thereporting events associated with a first shopper having a shoplift risklevel lower than a value (e.g., a predetermined threshold), and secondtimeline 3004 represents the product interaction events and thereporting events associated with a second shopper having a shoplift risklevel greater than the value. In other words, the determined likelihoodthat the second shopper will be involved in shoplifting is greater thanthe likelihood that the first shopper will be involved in shoplifting.

First timeline 3002 includes two types of product interaction events.The first type of product interaction event is represented as a blackdot and involves the first shopper picking a product from a shelf. Thesecond type of product interaction event is represented as a white dotand involves the first shopper returning a product to a shelf. In thedepicted example, after 20 minutes, the first shopper is going to acheckout area. During the 20 minutes of shopping in retail store 105,first shopper had picked sixteen products and returned four products.For each product interaction event, first shopper receives a reportrepresented in diagram 3000 as a flag. The report may include shoppingdata associated with a detected product interaction event. Consistentwith the present disclosure, the transmission of a report to the shopperis only one example of delivering the shopping data to shoppers. Theshopping data may also be delivered to shoppers by way of updating anonline interface accessible to the shoppers. Accordingly, the reportingevents may represent times in which the online interface are beingupdated. As shown, when the system determines that it unlikely that thefirst shopper will be involved in shoplifting the update rate fordelivering the shopping data is without any purposeful delays.

For the sake of illustration, second timeline 3004 includes the sameplurality of product interaction events as illustrated in first timeline3002, but, as shown, the update rate of the reporting events in secondtimeline 3004 is slower compared to the update rate in first timeline3002. Consistent with the present disclosure, the determined update rateof the second shopper includes an intended time delay in delivering theshopping data to the second shopper. A first delay 3006 represents theintended time delay introduced to the second shopper relative to thefirst shopper. The time delay may be applied from a time associated witha particular detected product interaction event. In some embodiments,the time delay increases as a determined likelihood that a shopper willbe involved in shoplifting increases. For example, after 12 minutes, thesystem had detected a suspicious action taken by the second shopper thatincreases the determined likelihood that the second shopper will beinvolved in shoplifting. Accordingly, second delay 3008 that representsthe purposeful time delay introduced to the second shopper after thedetection of the suspicious action, is greater than first delay 3006.For example, first delay 3006 may be included in a range of 10 secondsto one minute, and second delay 3008 may be included in a range of 1minute to 5 minutes. In addition, after the detection of the suspiciousaction the system may stop providing shopping data associated withproduct interaction events that involve the shopper returning a productto a shelf.

In disclosed embodiments, the shopping data provided to the secondshopper may include notifications regarding detected product interactionevents issued in response to a secondary trigger event. The secondarytrigger event may include a detection that the shopper has exited anaisle of the retail store, a detection that the shopper has entered acheckout area of the retail store, or expiration of a predetermined timeperiod. In the case illustrated in second timeline 3004, the secondshopper enters the checkout area, and he or she receives all theshopping data reports that were purposefully delayed.

FIG. 31 is a flowchart of an example process 3100 for controlling adetail level of shopping data provided to frictionless shoppers executedby a processing device of system 100, according to embodiments of thepresent disclosure. The processing device of system 100 may include atleast one processor within an image processing unit (e.g., server 135)or any processor associated with retail store 105. For purposes ofillustration, in the following description, reference is made to certaincomponents of system 100. It will be appreciated, however, that otherimplementations are possible and that any combination of components ordevices may be used to implement the exemplary method. It will also bereadily appreciated that the illustrated method can be altered to modifythe order of steps, delete steps, or further include additional steps,such as steps directed to optional embodiments.

In some embodiments, the processing device of system 100 may receiveimage data captured using one or more image sensors in a retail store.As discussed earlier, image sensors and various other types of sensorsmay be used to monitor inventory of products in retail store 105. By wayof example only, at step 3102 in FIG. 31 , a processing device (e.g.,processing device 202) may receive image data captured using one or moreimage sensors in a retail store. In some embodiments, the processingdevice may receive image data from one or more capturing devices 125 andsensor data from at least one shelf sensor (e.g., detection elements851). Specifically, the processing device may receive input from one ormore shelf sensors disposed on a surface of a retail shelf configured tohold one or more products in additional to the image data. The one ormore shelf sensors may include, for example, a weight-sensitive sensor,a touch-sensitive sensor, a pressure-sensitive sensor, a light-sensitivesensor, or any combination thereof.

In some embodiments, the processing device of system 100 may analyze theimage data to detect a shopper in the retail store. Consistent with thepresent disclosure, detecting a shopper in the retail store may includeassociated a single shopper with a plurality pf product interactionevents. Alternatively, detecting a shopper in the retail store mayinclude determining whether the shopper is a recognized shopper. In oneembodiment, a determination of whether the shopper is a recognizedshopper may be based on analysis of the received image data. Forexample, the determination of whether the shopper is a recognizedshopper may be based on a comparison of at least a portion of thereceived image data to image information stored in a recognized shopperdatabase (e.g., facial signatures). In an alternative embodiment, thedetermination of whether the shopper is a recognized shopper mayadditionally be based on an interaction with an electronic deviceassociated with the shopper. The electronic device may be an RFID tag, amobile device, or a smart basket associated with the shopper andconfigured to receive a shopper identification code from the shopper. Byway of example only, at step 3104 in FIG. 31 , the processing device mayanalyze the image data to detect a shopper in the retail store, forexample using visual person detection algorithms.

In some embodiments, the processing device of system 100 may determine alikelihood that the shopper will be involved in shoplifting. As usedherein, the term “likelihood” generally refers to the probability of anevent. The term “likelihood,” when used in reference to shoplifting,generally contemplates the estimated probability that an individual willbe involved in shoplifting from retail store 105. The individual may bethe one that does the actual lifting or the one that seeks out weakpoints of the frictionless shopping system of retail store 105. By wayof example only, at step 3106 in FIG. 31 , the processing device maydetermine the likelihood that the shopper will be involved inshoplifting. In one embodiment, the determination of the likelihood thatthe shopper will be involved in shoplifting may be based on analysis ofthe received image data. For example, detection of an avoidance actiontaken by the shopper to avoid at least one store associate may result ina higher determined shoplift risk level than where the shopper is notdetected as engaging in an avoidance action. In another embodiment, thedetermination of the likelihood that the shopper will be involved inshoplifting may be based on outcome of an attempt to recognize theshopper. For example, a determination that the shopper is a recognizedshopper may result in a first determined shoplift risk level lower thana second determined shoplift risk level resulting from a determinationthat the shopper is not a recognized shopper. In another embodiment, thedetermination of the likelihood that the shopper will be involved inshoplifting may be based on an estimated age of the shopper, which maybe determined based on the analysis of the image data. For example, afirst estimated age may be associated with a first determined shopliftrisk level that is lower than a second determined shoplift risk levelassociated with a second estimated age, wherein the first estimated ageis greater than the second estimated age. In another embodiment, thedetermination of the likelihood that the shopper will be involved inshoplifting may be based on a shopping history for the shopper retrievedfrom a shopping history database. The shopping history for the shoppermay indicate that the shopper is associated with previous questionableconduct. In a first example, the shopping history database may storefacial signatures of shoppers that previously visited the retail store.The facial signatures may be used in identifying the shopper via ananalysis of the image data. In a second example, the shopping historydatabase may store a history of returns made to the retail store by theshopper. The history of returns may be used in determining thelikelihood that the shopper will be involved in shoplifting. In someexamples, the trajectory of the shopper in the retail store may bedetermined, for example by analyzing the image data to detect positionsof the shopper in the retail store at different times (for example usingvisual person detection algorithms), and combining the detectedpositions of the shopper into a trajectory, for example using a Kalmanfilter to remove false detections from the trajectory. Further, thetrajectory may be analyzed, for example using a classification or aregression model that takes a trajectory as an input, to determine thelikelihood that the shopper will be involved in shoplifting. Such modelmay be a machine learning model trained using training example todetermine likelihood of shoplifting (binary ‘likely’ or ‘not likely’ ina case of a binary classification model, discrete likelihood in a caseof a multiclass classification model, continuous likelihood in a case ofa regression model, etc.) using training examples. An example of suchtraining example may include a sample trajectory together with a labelindicating the likelihood of shoplifting corresponding to the sampletrajectory. In some examples, the image data may be analyzed to detectactions of the shopper, for example using visual action recognitionalgorithms. Further, the likelihood that the shopper will be involved inshoplifting may be determined based on the detected actions. Forexample, each action may be assigned a suspicion score, for examplebased on a type of the action or other properties of the action, and thelikelihood that the shopper will be involved in shoplifting may bedetermined based on a statistical measure (such as maximum, minimum,median, mean, mode, variance, etc.) or other function of the suspicionscores corresponding to the suspicion scores. In some examples, imagedata of the shopper may be analyzed to determine stress level of theshopper (for example using a visual classification model, or based onfacial expression, based on perspiration, etc.). Further, the likelihoodthat the shopper will be involved in shoplifting may be determined basedon the determined stress level. For example, a stressed shopper may beassigned a higher likelihood that the shopper will be involved inshoplifting than a non-stressed shopper that has otherwise similarcharacteristics.

In some embodiments, the processing device of system 100 may control adetail level associated with frictionless shopping data provided to theshopper based on the determined likelihood that the shopper will beinvolved in shoplifting. In this disclosure, the term “controlling adetail level associated with frictionless shopping data” includesdetermining the frictionless shopping data for the shopper and selectinghow much of the determined frictionless shopping data to share with theshopper. In one embodiments, the determined frictionless shopping dataincludes an indication of products currently included in a virtualshopping cart associated with the shopper. For example, the virtualshopping cart may update as the shopper selects products from a retailshelf and when the shopper returns. By way of example only, at step 3108in FIG. 31 , the processing device may control a detail level associatedwith frictionless shopping data provided to the shopper based on thedetermined likelihood that the shopper will be involved in shoplifting.Generally, more detail is delivered in the provided frictionlessshopping data at a first determined shoplift risk level than at a seconddetermined shoplift risk level, wherein the second determined shopliftrisk level is higher than the first determined shoplift risk level.

In an example, the frictionless shopping data provided in conjunctionwith the first shoplift risk level may identify both a quantity and atype of a particular product selected by the shopper, but thefrictionless shopping data provided in conjunction with the seconddetermined shoplift risk level may identify a type, but not a quantity,of a particular product selected by the shopper. In another example, thefrictionless shopping data provided in conjunction with the firstdetermined shoplift risk level may identify a type of a particularproduct selected by the shopper, but the frictionless shopping dataprovided in conjunction with the second determined shoplift risk levelmay identify a general class, but not a specific type, of a particularproduct selected by the shopper. In yet another example, thefrictionless shopping data provided in conjunction with the firstdetermined shoplift risk level may identify product interaction eventsin which products were removed from a retail shelf and also may identifyproduct interaction events in which products were returned to a retailshelf, but wherein the frictionless shopping data provided inconjunction with the second determined shoplift risk level may identifyproduct interaction events in which products were removed from a retailshelf but does not identify product interaction events in which productswere returned to a retail shelf. In a fourth example, the frictionlessshopping data provided in conjunction with the first determined shopliftrisk level may include a notification sent to the shopper in response toeach detected product interaction event involving the shopper, butwherein the frictionless shopping data provided in conjunction with thesecond determined shoplift risk level includes notifications regardingdetected product interaction events that are issued in response to asecondary trigger event, rather than in response to detection ofindividual product interaction events.

FIG. 32 is a flowchart of an example process 3200 for deliveringshopping data to frictionless shoppers executed by a processing deviceof system 100, according to embodiments of the present disclosure. Theprocessing device of system 100 may include at least one processorwithin image processing unit (e.g., server 135) or any processorassociated with retail store 105. For purposes of illustration, in thefollowing description, reference is made to certain components of system100. It will be appreciated, however, that other implementations arepossible and that any combination of components or devices may be usedto implement the exemplary method. It will also be readily appreciatedthat the illustrated method can be altered to modify the order of steps,delete steps, or further include additional steps, such as stepsdirected to optional embodiments.

As discussed above with reference to step 3102 in FIG. 31 , in someembodiments, the processing device of system 100 may receive image datacaptured using one or more image sensors in a retail store. Accordingly,at step 3202, the processing device may receive image data capturedusing one or more image sensors in a retail store. In some embodiments,the processing device of system 100 may analyze the image data toidentify a plurality of product interaction events for at least oneshopper in the retail store. The plurality of product interaction eventsmay be associated with a single shopper or with multiple shoppers.Consistent with the present disclosure, the step of identifying theplurality of product interaction events may include identifying everyshopper in retail store 105 and determining for each shopper a pluralityof product interaction events that represent all the products that theshopper interacted with (e.g., picked from a shelf or returned to ashelf). By way of example only, at step 3204 in FIG. 32 , the processingdevice may analyze the image data to identify a plurality of productinteraction events for at least one shopper in the retail store. Theplurality of product interaction events may involve the at least oneshopper taking a product from a shelf and may also involve the at leastone shopper returning a product to a shelf.

In some embodiments, the processing device of system 100 may determineshopping data associated with the plurality of product interactionevents. In one embodiment, the determination of the shopping data mayinclude determining a frictionless checkout eligibility status ofshopper. For example, a successful identification of the product in aproduct interaction may result in an eligible frictionless checkoutstatus, and a failed identification of the product in a productinteraction may result in an ineligible frictionless checkout status. Inanother embodiment, the determination of the shopping data may includean identification of a type of product associated with a productinteraction event. By way of example only, at step 3206 in FIG. 32 , theprocessing device may determine shopping data associated with theplurality of product interaction events.

As discussed above with reference to step 3106 in FIG. 31 , in someembodiments, the processing device of system 100 may determine alikelihood that the at least one shopper will be involved inshoplifting. Accordingly, at step 3208, the processing device maydetermine a likelihood that the at least one shopper will be involved inshoplifting. The different ways that the processing device may determinethe likelihood that a shopper will be involved in shoplifting, asdiscussed above with reference to step 3106, may also be implemented inprocess 3200.

In some embodiments, the processing device of system 100 may determinean update rate for updating the at least one shopper with the shoppingdata based on the determined likelihood. As used herein, the term“update rate” generally refers to how often shopping data is deliveredto the at least one shopper. Consistent with the present disclosure,determining the update rate for delivering of shopping data to the atleast one shopper may include determining how often shopping data may betransmitted to a communication device associated with the at least oneshopper or how often an online interface accessible to the at least oneshopper will be updated with new shopping data. By way of example only,at step 3210 in FIG. 32 , the processing device may determine an updaterate for updating the at least one shopper with the shopping data basedon the determined likelihood. Generally, wherein a first shoplift risklevel is higher than a second determined shoplift risk level, a shoppingdata may be provided at a first update rate for a shopper associatedwith the first shoplift risk level that is lower than a second updaterate for a shopper associated with the second shoplift risk level. Thedetermination of the update rate may include determining a time delay toapply in delivering the shopping data to the at least one shopper. Forexample, the delay may be included in a range of 10 seconds to 1 minuteor in a range of 1 minute to 5 minutes. In one embodiment, the delay mayincrease as a determined likelihood that the at least one shopper willbe involved in shoplifting increases, for example, the determinedlikelihood may change upon detecting of an action of the at least oneshopper. Alternatively, the delay may decrease as a determinedlikelihood that the at least one shopper will be involved in shopliftingdecreases. In another embodiment, the time delay may be applied from atime associated with a particular detected product interaction event.

In some embodiments, the processing device of system 100 may deliver theshopping data to the at least one shopper at the determined update rate.As mentioned above, the shopping data delivered to the at least oneshopper may be indicative of products currently found in at least onevirtual shopping cart associated with the at least one shopper or thefrictionless checkout eligibility status of shopper. By way of exampleonly, at step 3212 in FIG. 32 , the processing device may deliver theshopping data to the at least one shopper at the determined update rate.The determined update rate may be higher than a default update rate orlower than default update rate. In addition, the processing device mayuse two (or more) determined update rates for delivering the shoppingdata. For example, a first determined update rate may be used fordelivering a first type of shopping data (e.g., indications that a userpicked a product from a shelf), and a second determined update rate maybe used for delivering a second type of shopping data (e.g., indicationsthat a user returned a product to a shelf). The first determined updaterate may be different (e.g., it may be greater or lower) than the seconddetermined update rate.

While frictionless shopping technology develops, store associates willneed to continue to manually scan unidentified products, resolve productinteraction ambiguities, etc. Forced interactions with checkout clerks,however, may negatively impact the shopping experience, especially insituations where an entire shopping cart may be disqualified fromfrictionless shopping eligibility due to the presence of even one or afew ambiguous items. A retail store may wish to minimize the need forhuman interaction in checkout and maximize the availability offrictionless shopping, even if for portions of the items in a shopper'scart. One way to reduce the requirement for human interaction and tomaximize the number of products available for frictionless shopping isto track frictionless shopping disqualification events on a per shoppingreceptacle basis. For example, if a particular shopper is associatedwith an ambiguous product interaction event, that event may disqualifyonly one of the shopper's receptacles from frictionless shoppingeligibility. The shopper's other shopping receptacles may remaineligible for frictionless shopping. In this way, the proposed system mayreduce the number of receptacles and the number of products that must bemanually scanned by a store clerk. The following description is directedto this concept.

As described above, a frictionless checkout refers to a checkout processthat eliminates or reduces the need to take inventory of products beingpurchased by the shopper at checkout. Consistent with some embodiments,the frictionless checkout process may be a full frictionless checkoutprocess or a semi frictionless checkout process. A full frictionlesscheckout process may exclude any interaction between the shopper and astore associate or checkout device. For example, the shopper may walkout of the store with the selected products and a payment transactionmay occur automatically. In contrast, a semi frictionless checkoutprocess may include some kind of interaction between the shopper and astore associate or checkout device regarding some of the selectedproducts, but not to all of the selected products. In one example thesystem may make a decision if there is a need to take inventory ofproducts being purchased by the shopper at checkout on per shoppingreceptacle basis. This example is described below with reference to FIG.33A.

FIG. 33A depicts two shoppers 3300 (e.g., a first shopper 3300A and asecond shopper 3300B) standing in a checkout area of retail store 105.Each of the depicted shoppers is associated with a plurality of shoppingreceptacles. Specifically, first shopper 3300A is associated with (e.g.,uses) five shopping receptacles: a cart 3302 and four reusable shoppingbags 3304A-D. Second shopper 3300B is associated with (e.g., caries) twoshopping receptacles: shopping basket 3306A and shopping basket 3306B.Consistent with this disclosure, the term “shopping receptacle” is usedto describe any form of container in which shoppers can place products.In one example, the capacity of a shopping receptacle may vary between10 liters and 500 liters. According to one embodiment, a shoppingreceptacle associated with shopper 3330 may include a shopping bag. Insome examples, the shopping bag may be a paper bag, a plastic bag, areusable shopping bag, or a biodegradable bag. According to anotherembodiment, a shopping receptacle associated with shopper 3330 mayinclude a shopping cart. In some examples, the shopping cart may be apersonal folding shopping cart or a conventional store cart, asillustrated in the figure. According to another embodiment, a shoppingreceptacle associated with shopper 3330 may include a section of ashopping cart. For example, some shopping carts may have spacers ordividers such that a particular section of the shopping cart (e.g., rearsection, middle section, and front section) may be treated as a separateshopping receptacle. According to another embodiment, a shoppingreceptacle associated with shopper 3330 may include a shopping basket.The listed types of shopping receptacles should be considered asinclusive examples of shopping receptacles. Other types shoppingreceptacles may be used and, in some embodiments, a particular shoppermay be associated (e.g. use) any one or more of a particular type ofshopping receptacle.

For example, consistent with the present disclosure, a shopper mayconcurrently use multiple shopping receptacles having the same type ordifferent types. As shown in FIG. 33A, first shopper 3300A uses shoppingcart 3302 and shopping bags 3304, and second shopper 3300B uses onlyshopping baskets 3306. The system may be programmed to distinguishbetween shopping receptacles of a single shopper and to track theproducts being placed in each of the shopping receptacles. In oneembodiment, each of the shopping receptacles used by shopper 3300 mayinclude a unique identifier enabling the system to automaticallydistinguish shopping receptacles from each another. Examples of uniqueidentifiers may include a visual code (such as a barcode, a QR code,etc.), an RFID tag, or a text. Consistent with the present disclosure,the system may use captured image data to automatically distinguishshopping receptacles from each other. In one embodiment, the image datamay include images captured by at least one image sensor fixedly mountedto a store shelf in retail store 105, such as a plurality of stationarycapturing devices 125. In another embodiment, the image data may includeimages captured by at least one image sensor fixedly mounted to ashopping cart in the retail store, such as an image sensor 3308. Thefield of view of image sensor 3308 may be directed to the inside of theshopping cart for capturing a plurality of shopping receptacles.

The system may be configured to identify product interaction events andto associate each event to one of a plurality of shopping receptaclesassociated with the shopper. In one embodiment, the system may identifythe plurality of product interaction events solely based on image datasuch as, for example, image data received from a plurality of imagesensors fixedly mounted in retail store 105 and/or an image sensorfixedly mounted to the shopping cart. Alternatively, the system mayidentify the plurality of product interaction events using a combinationof image data and data from one or more shelf sensors. For example,sensor data received from detection elements attached to store shelvesmay be used to identify which product was picked from a shelf and imagedata from image sensor 3308 may be used to associate the identifiedproduct with the appropriate shopping receptacle. If the system fails toidentify the product inserted to a shopping receptacle, that shoppingreceptacle may be determined as ineligible for frictionless checkout.Accordingly, the system may cause delivery of an indicator identifyingwhich of the shopper's shopping receptacles is ineligible forfrictionless checkout. In one embodiment, the indicator may be deliveredto a computing device associated with a store associate of the retailstore. For example, with reference to FIG. 33A, the indicator may bedelivered to a store associate 3310 via smart glasses 3312.

FIG. 33B illustrates an example visual indicator showing thefrictionless checkout eligibility status of a shopping receptacle.Specifically, the frictionless eligibility indicator may identify whichof the shopping receptacles associated with a particular shopper isineligible for frictionless checkout. Consistent with the presentdisclosure, a frictionless eligibility indicator may be delivered to acommunication device. In one embodiment, the communication device may bea wearable device associated with a shopper, for example, a mobiledevice (e.g., smartphone, smartwatch, or a store pager that the shoppercollects when entering retail store 105). In another embodiment, thecommunication device may be a wearable device may be associated with astore associate. FIG. 33B depicts the view from smart glasses 3312 ofstore associate 3310. As shown, a visual indicator 3350 may overlay theimage of shopping bag 3304C and may indicate that shopping bag 3304C isineligible for frictionless checkout. The absence of visual indicator3350 overlaying the image of shopping bags 3304A, 3304B, and 3304D, andcart 3302 may indicate these shopping receptacles are eligible forfrictionless checkout. Based on the delivered indicator, store associate3310 may asked shopper 3300A to take out all the items only fromshopping bag 3304C (i.e., not from shopping bags 3304A, 3304B, and3304D, and cart 3302) in order to scan them manually.

In disclosed embodiments, the visual indicator may be delivered to amobile device associated with the shopper. For example, after firstshopper 3300A enters a checkout area, he or she may receive a textmessage to his or her smartphone indicating that reusable shopping bags3304A-C are eligible for frictionless checkout and reusable shopping bag3304D is ineligible for frictionless checkout. In other embodiments, theindicator may be delivered by a display associated with a shopping cartused by the shopper. For example, the indicator may be delivered tocommunication device 2710B illustrated in FIG. 27 . In otherembodiments, the indicator may be delivered to a computing deviceassociated with a store associate of the retail store. For example, theindicator may include an image of the ineligible shopping receptacle andmay be delivered to a cash register device 3352. The indicator deliveredto cash register device 3352 may be displayed on a first display 3354pointing to the shopper and/or to a second display 3356 pointing to thecashier. In other embodiments, the indicator may be delivered via afeedback device associated with a shopping receptacle. The feedbackdevice may be a display device, a haptic feedback device, a lightemitting device, or an audio feedback device. For example, the shoppingreceptacle may be a shopping basket that provides a color indication ofthe shopping receptacle's eligibility for frictionless checkout.Specifically, a green light may indicate that products in the shoppingreceptacle may be eligible for frictionless checkout; and a red lightmay indicate that products in the shopping receptacle may be ineligiblefor frictionless checkout.

FIG. 34 depicts a flow diagram illustrating an example process 3400 fordetermining the frictionless checkout eligibility statuses of twoshopping receptacles. For purposes of illustration, the shopper has onlytwo shopping receptacles. It will be appreciated, however, that process3400 may be expended to cover cases where the shopper has more than twoshopping receptacles. In the following description, reference is made tocertain components of system 100, yet other components of the system maybe used to implement example process 3400. It will also be readilyappreciated that the example process 3400 can be altered to modify theorder of steps, delete steps, or add additional steps.

Process 3400 begins when the processing device analyzes captured imagedata to identify a shopper at retail store 105 with a first shoppingreceptacle and a second shopping receptacle (block 3402). Examples ofshopping receptacles are described above with reference to FIG. 33A.Thereafter, the processing device may set the frictionless checkoutstatuses of the first shopping receptacle and the second shoppingreceptacle as eligible (block 3404). The processing device may determineif the shopper has entered to the store's checkout area or continuesshopping (decision block 3406). As long as the shopper continuesshopping in retail store 105, the processing device may detect productinteraction events (block 3408). In disclosed embodiments, theprocessing device may detect product interaction events based onanalysis of captured image data. In other embodiments, the processingdevice may obtain sensor data from one or more shelf sensors, and thedetecting of product interaction events may be based on analysis of theimage data and the sensor data. For example, the sensor data may beobtained from a weight-sensitive sensor, a touch-sensitive sensor, apressure-sensitive sensor, a light-sensitive sensor, or any combinationthereof. Consistent with the present disclosure, detecting a productinteraction event may include determining whether the productinteraction event involves the first shopping receptacle or involves thesecond shopping receptacle. In a first example, the product interactionevent may include the shopper removing a product from a shelf andinserting it to the first shopping receptacle. In a second example, theproduct interaction event may include the shopper returning a product toa shelf from the second shopping receptacle.

After block 3408, the process may split into two identical branchesbased on the association of the shopping receptacle to the productinteraction event. In each of the branches, the processing device maydetermine if the product interaction event affects the frictionlesseligibility of the corresponding shopping receptacle. This determinationmay involve one or more steps illustrated in decision blocks 3410-3416and 3420-3426. According to one embodiment, the processing device mayattempt to identify a type of product involved in the detected productinteraction event (decisions blocks 3410 and 3420). To identify the typeof product involved in the detected product interaction event, theprocessing device may use any method known in the art including methodsdescribed herein. When the processing devices fails to identify the typeof product, the process may move to block 3418 or block 3428, and theprocessing device may set the frictionless checkout status of therespective shopping receptacle as ineligible. When the processing devicesucceeds in identifying the type of product involved in the detectedproduct interaction event, the process may continue to decision block3412 or to decision block 3422, respectively.

Process 3400 may resume when the processing device determines if atleast one indicator of a degree of ambiguity associated with thedetected product interaction event is greater than a threshold(decisions blocks 3412 and 3422). The at least one indicator of a degreeof ambiguity associated with the product interaction event may bedetermined based on the image data, based on the sensor data, or acombination thereof. When the at least one indicator of a degree ofambiguity associated with the product interaction event is greater thana threshold, the process may move to block 3418 or block 3428, and theprocessing device may set the frictionless checkout status of therespective shopping receptacle as ineligible. When the at least oneindicator of a degree of ambiguity associated with the productinteraction event is less than a threshold, the process may continue todecision block 3414 or to decision block 3424, respectively.

Process 3400 may resume when the processing device determines if theproduct associated with the detected product interaction event isdesignated as ineligible for frictionless checkout (decisions blocks3414 and 3424). In one example, certain products (e.g., fresh fruits)may be categorically designated as ineligible for frictionless checkout.In another example, products collected from a shelf, or a portion of ashelf temporarily designated as ineligible for frictionless checkout mayalso cause products to be designated as ineligible. When the shopperselects a product designated as ineligible for frictionless checkout,the process may move to block 3418 or block 3428, and the processingdevice may set the frictionless checkout status of the respectiveshopping receptacle as ineligible. When the shopper selects a productdesignated as eligible for frictionless checkout, the process maycontinue to decision block 3416 or to decision block 3426, respectively.

Process 3400 may resume when the processing device determines if aproduct value associated with the detected product interaction eventexceeds a predetermined threshold (decisions blocks 3416 and 3426).Consistent with disclosed embodiments, the processing device mayidentify that the shopper picked a product, but failed to recognizewhich product was picked. In such a situation it may be in the retailstore interest to encourage frictionless checkout for one or morereasons. For example, proceeding with frictionless checkout may savestore associates resources and/or increase the customer's satisfaction.Therefore, for example, if a shopper picked a product from a shelf inwhich all the products costs less than a predetermined threshold (e.g.,a product costing less than $2), the processing device may maintain theeligible frictionless status of the shopping receptacle even when thespecific product was not identified. In another example, thepredetermined threshold may be up to 5% of a total product valueassociated with products selected by the shopper. When the product valueexceeds the predetermined threshold, the process may continue to block3418 or block 3428, and the processing device may set the frictionlesscheckout status of the respective shopping receptacle as ineligible.When the product value is less than (or equal to) the predeterminedthreshold, the frictionless checkout status of the first shoppingreceptacle and the second shopping receptacle may be kept as eligibleand the process may next determine if the shopper has entered to thecheckout area or continues shopping (decision block 3406).

When the processing device determines that the shopper has entered tothe checkout area, the processing device may determine if thefrictionless checkout status of at least one of the shopping receptacleis ineligible (decision block 3430). When the frictionless checkoutstatuses of all the shopping receptacles associated with the shopper areeligible, the processing device may enable the shopper a completefrictionless checkout (block 3432). When the frictionless checkoutstatus of at least one of the shopping receptacles is ineligible, theprocessing device may require a manual checkout to a specific ineligibleshopping receptacle (block 3434). For example, when the first shoppingreceptacle is determined to be eligible for frictionless shopping andthe second shopping receptacle is determined to be ineligible forfrictionless shopping, the processing device may require a manualcheckout action with respect to only the second shopping receptacle.

In another embodiment, prior to executing the step of block 3434, theprocessing device may cause an ambiguity resolution action in responseto a determination that one of shopping receptacle has an ineligiblecheckout status due to an ambiguous product interaction event. In oneexample, causing the ambiguity resolution action may include issuing aquery to the shopper to confirm the identity of products in anineligible shopping receptacle. The query may be answered using adedicated device for scanning a barcode or placing the products in frontof a camera. In another example, when a shopping receptacle correspondsto a shopping list, the shopping list may be used to reduce a degree ofambiguity associated with the product interaction event. Other examplesof ambiguity resolution actions that may resolve ambiguous productinteraction events are described above. After completing the ambiguityresolution action, the processing device may restore the frictionlesscheckout eligibility status of one or more shopping receptacles inquestion.

In a related embodiment, the ambiguity resolution action may includecompleting a manual checkout to one of the shopping receptacles.Specifically, in some cases, ambiguity resolution actions, such asnon-frictionless checkout of one of the shopping receptacles, mayresolve the ambiguity with respect to the other shopping receptacles.Consistent with the present disclosure, processing device may detect aproduct interaction event involving an ambiguity between the firstshopping receptacle and the second shopping receptacle. The processingdevice may access data related to the first shopping receptacle and datarelated to the second shopping receptacle to select one of the first andsecond shopping receptacles. Thereafter, the processing device may causedelivery of an indicator identifying that the selected shoppingreceptacle is ineligible for frictionless checkout. The selection ofwhich of the shopping receptacle to identify as ineligible forfrictionless checkout may be based, at least in part, on quantity ofproducts corresponding to each shopping receptacle, on prices ofproducts corresponding to each shopping receptacle, on degree ofambiguity corresponding to each shopping receptacle, and more.Additionally, the selection of the shopping receptacle may be made afterdetecting the product interaction event involving an ambiguity betweenthe first shopping receptacle and the second shopping receptacle.Alternatively, the selection of the shopping receptacle may be madeafter additional product interaction events occur.

FIG. 35 is a flowchart of an example process 3500 for trackingfrictionless shopping eligibility relative to individual shoppingreceptacles executed by a processing device of system 100, according toembodiments of the present disclosure. The processing device of system100 may include at least one processor within an image processing unit(e.g., server 135) or any processor associated with retail store 105.For purposes of illustration, in the following description, reference ismade to certain components of system 100. It will be appreciated,however, that other implementations are possible and that anycombination of components or devices may be used to implement theexemplary method. It will also be readily appreciated that theillustrated method can be altered to modify the order of steps, deletesteps, or add additional steps, such as steps directed to optionalembodiments.

In disclosed embodiments, the processing device of system 100 may obtainimage data captured using a plurality of image sensors positioned in aretail store. As discussed earlier, image sensors and various othertypes of sensors may be used to detect product interaction events inretail store 105. These sensors may include weight sensors, touchsensors, pressure sensors, light sensors, and more. By way of example,at step 3502 in FIG. 35 , a processing device (e.g., processing device202) may obtain image data captured using a plurality of image sensorspositioned in a retail store. In some embodiments, the image data mayinclude images captured by at least one image sensor fixedly mounted inthe retail store. The at least one image sensor may be fixedly mountedto a store shelf or to other objects in retail store 105 (such as walls,ceilings, floors, refrigerators, checkout stations, displays,dispensers, rods which may be connected to other objects in retail store105, and so forth). In other embodiments, the image data may includeimages captured by at least one image sensor fixedly mounted to ashopping cart in the retail store. For example, the at least one imagesensor may be mounted such it captures the inside of the shopping cart.

In disclosed embodiments, the processing device of system 100 mayanalyze the image data to identify a shopper at one or more locations ofthe retail store. The shopper may be associated with a plurality ofshopping receptacles. Consistent with the present disclosure, at leastone of the first shopping receptacle or the second shopping receptaclemay be a shopping bag, a shopping cart, a section of a shopping cart, ora shopping basket. By way of example, at step 3504 in FIG. 35 , theprocessing device may analyze the image data to identify a shopper atone or more locations of the retail store. In one embodiment, each ofthe first shopping receptacle and the second shopping receptacle mayinclude a unique identifier enabling the first shopping receptacle andthe second shopping receptacle to be automatically distinguished fromone another by the system. The unique identifier may include a visualcode (e.g., a barcode, a QR code, a serial number, a color code, etc.).Alternatively, the unique identifier may include an RFID tag or text. Insome examples, a machine learning model may be trained using trainingexamples to transform images of shopping receptacles to mathematicalobjects in a mathematical space. The transformation may be configured,through the training, to transform different images of the same shoppingreceptacle (for example, images taken from different viewpoints, atdifferent illumination conditions, etc.) to nearby mathematical objectsin the mathematical space, and to transform images of different shoppingreceptacle to mathematical objects that are distant from one another inthe mathematical space. For example, a training example may include twoimages of the same shopping receptacle, and a loss function used in thetraining of the machine learning model for that pair of images may bemonotonically increasing function of the distance between the twomathematical objects corresponding to the two images. In anotherexample, a training example may include two images of two differentshopping receptacles, and a loss function used in the training of themachine learning model for that pair of images may be monotonicallydecreasing function of the distance between the two mathematical objectscorresponding to the two images. The trained machine learning model maybe used to transform images of shopping receptacle to mathematicalobjects in the mathematical space, a clustering algorithm may be used toidentify groups of mathematical objects in the mathematical space thatcorrespond to specific shopping receptacles, and the determination thatan image (or a portion of an image) is an image of a particular shoppingreceptacle may be based on an association of the mathematical objectcorresponding to the image to a particular cluster corresponding to theparticular shopping receptacle. In some examples, visual trackingalgorithms may be used to track shopping receptacle in a video, and todetermine that a particular shopping receptacle in a first frame of thevideo is the same shopping receptacle seen in a particular position in asecond frame of the video. In some examples, a convolution of at leastpart of an image of a shopping receptacle may be calculated. In responseto a first value of the convolution, it may be determined that theshopping receptacle is a first shopping receptacle, and in response to asecond value of the convolution, it may be determined that the shoppingreceptacle is a second shopping receptacle, the second shoppingreceptacle may differ from the first shopping receptacle.

In disclosed embodiments, the processing device of system 100 may detecta first product interaction event involving a first shopping receptacleassociated with the shopper and a second product interaction eventinvolving a second shopping receptacle associated with the shopper. Theproduct interaction events may include the shopper removing a productfrom a shelf associated with the retail store or returning a productfrom a shelf associated with the retail store. In one embodiment, thedetection of at least one of the first or second product interactionevent may be based solely on an analysis of the image data. In anotherembodiment, the detection of at least one of the first or second productinteraction event may be based on an analysis of the image data andsensor data (e.g., the sensor data may be obtained from a one or moresensors disposed on a retail shelf between the retail shelf and one ormore products placed on the retail shelf). By way of example, at step3506 in FIG. 35 , the processing device may detect a first productinteraction event involving a first shopping receptacle associated withthe shopper and a second product interaction event involving a secondshopping receptacle associated with the shopper. Consistent with thepresent disclosure, detecting a product interaction event may includedetermining a degree of ambiguity associated with the first productinteraction event. The degree of ambiguity associated with at least oneof the first product interaction event and the second productinteraction event may be determined based on the image data or based ona combination of image data and sensor data.

In disclosed embodiments, the processing device of system 100 maydetermine whether each of the shopping receptacles associated with theshopper is eligible for frictionless checkout. The determination may bebased on the detected product interaction events. By way of example, atstep 3508 in FIG. 35 , the processing device may determine whether thefirst shopping receptacle is eligible for frictionless checkout based onthe detected first product interaction event, and at step 3510 theprocessing device may determine whether the second shopping receptacleis eligible for frictionless checkout based on the detected secondproduct interaction event. For example, if the first shopping receptacleis determined to be eligible for frictionless shopping and the secondshopping receptacle is determined to be ineligible for frictionlessshopping, the processing device may require a manual checkout actionwith respect to only the second shopping receptacle. In one embodiment,the determination that the first shopping receptacle is eligible forfrictionless checkout may be based on at least one indicator of a degreeof ambiguity associated with the first product interaction event. Forexample, if the degree of ambiguity associated with the second productinteraction event is high, the processing device may determine that thesecond shopping receptacle is ineligible for frictionless checkout. Inanother embodiment, the determination that the first shopping receptacleis eligible for frictionless checkout may be based on whether thedetected first product interaction event includes the shopper selectinga product that is designated as ineligible for frictionless checkout.For example, a product may be picked from a shelf designated asineligible for frictionless checkout. In another embodiment, thedetermination that the first shopping receptacle is ineligible forfrictionless checkout may be based on a determination of a product valueassociated with the first product interaction event. Specifically, ashopping receptacle may be determined to be ineligible for frictionlesscheckout if the estimated product value associated with productinteraction event exceeds a predetermined threshold. In one example, thepredetermined threshold may be up to $1.5 for a single product. Inanother example, the predetermined threshold may be up to 4.5% of atotal product value associated with products selected by the shopper.

In disclosed embodiments, in response to a determination that the firstshopping receptacle or the second shopping receptacle is ineligible forfrictionless checkout, the processing device of system 100 may causedelivery of an indicator identifying which of the first shoppingreceptacle or the second shopping receptacle is ineligible forfrictionless checkout. By way of example, at step 3512 in FIG. 35 , theprocessing device may cause delivery of an indicator identifying whichof the first shopping receptacle or the second shopping receptacle isineligible for frictionless checkout. In one embodiment, the indicatoridentifying which of the first shopping receptacle or the secondshopping receptacle is ineligible for frictionless checkout may bedelivered to a wearable device associated with the shopper. In anotherembodiment, the indicator identifying which of the first shoppingreceptacle or the second shopping receptacle is ineligible forfrictionless checkout may be delivered to a mobile device associatedwith the shopper. In another embodiment, the indicator identifying whichof the first shopping receptacle or the second shopping receptacle isineligible for frictionless checkout may be delivered by a displayassociated with a shopping cart used by the shopper. In anotherembodiment, the indicator identifying which of the first shoppingreceptacle or the second shopping receptacle is ineligible forfrictionless checkout may be delivered to a computing device associatedwith a store associate of the retail store. In another embodiment, theindicator identifying which of the first shopping receptacle or thesecond shopping receptacle is ineligible for frictionless checkout maybe delivered to the shopper after the shopper enters a checkout area ofthe retail store. In another embodiment, the indicator identifying whichof the first shopping receptacle or the second shopping receptacle isineligible for frictionless checkout may be delivered to the shopperbefore the shopper enters a checkout area of the retail store. Inanother embodiment, the indicator identifying which of the firstshopping receptacle or the second shopping receptacle is ineligible forfrictionless checkout may be delivered via a feedback device associatedwith at least one of the first shopping receptacle and the secondshopping receptacle. The feedback device may be a display device, ahaptic feedback device, a light emitting device, or an audio feedbackdevice.

In disclosed embodiments, the processing device may cause an ambiguityresolution action in response to a detection of at least one ambiguousinteraction event among the first product interaction event or thesecond product interaction event. Thereafter, the processing device maycause an eligibility status for frictionless checkout for the firstshopping receptacle or the second shopping receptacle to be restoredbased on data associated with a completion of the ambiguity resolutionaction. In one embodiment, the ambiguity resolution action may include arequest to answer a query to confirm identity of products involved inthe at least one ambiguous interaction event. In another embodiment,when a shopping receptacle corresponds to a shopping list, the shoppinglist may be used to reduce a degree of ambiguity associated with theproduct interaction event. Specifically, when the first shoppingreceptacle corresponds to a first shopping list and the second shoppingreceptacle corresponds to a second shopping list (e.g., different fromthe first shopping list), the first shopping list may be used to reducea degree of ambiguity associated with the first product interactionevent, and the second shopping list may be used to reduce a degree ofambiguity associated with the second product interaction event.

In some case, manual checkout of one shopping receptacle may resolve theambiguity with respect to another shopping receptacle. Thus, the systemmay identify only one of the two shopping receptacles as ineligible forfrictionless checkout. Specifically, the processing device may detect,based on the analysis of the image data, a third product interactionevent involving an ambiguity between the first shopping receptacle andthe second shopping receptacle. Thereafter, the processing device mayaccess data related to the first shopping receptacle and data related tothe second shopping receptacle to select one of the first and secondshopping receptacle. Then the processing device may select which of thefirst shopping receptacle and the second shopping receptacle to classifyas ineligible for frictionless checkout. The processing device may nextcause delivery of an indicator identifying that the selected shoppingreceptacle is ineligible for frictionless checkout. In one embodiment,the selection may be made after the third product interaction event,while in other embodiment the selection may be made after additionalproduct interaction events are detected. For example, the selection maybe made when the shopper enters to the checkout area (e.g., within apredefined distance from a checkout device). Moreover, the selection maybe based, at least in part, on the quantity of products corresponding toeach shopping receptacle, on the prices of products corresponding toeach shopping receptacle, or on the degree of ambiguity corresponding toeach shopping receptacle.

As noted generally above, a retail environment may provide africtionless checkout experience. As used herein, a frictionlesscheckout refers to any checkout process for a retail environment with atleast one aspect intended to expedite, simplify, or otherwise improve anexperience for customers. In some embodiments, a frictionless checkoutmay reduce or eliminate the need to take inventory of products beingpurchased by the customer at checkout. For example, this may includetracking the selection of products made by the shopper so that they arealready identified at the time of checkout. The tracking of products mayoccur through the implementation of sensors used to track movement ofthe shopper and/or products within the retail environment, as describedthroughout the present disclosure. Additionally or alternatively, africtionless checkout may include an expedited or simplified paymentprocedure. For example, if a retail store has access to paymentinformation associated with a shopper, the payment information may beused automatically or upon selection and/or confirmation of the paymentinformation by the user. In some embodiments, a frictionless checkoutmay involve some interaction between the user and a store associate orcheckout device or terminal. In other embodiments, the frictionlesscheckout may not involve any interaction. For example, the shopper maywalk out of the store with the selected products and a paymenttransaction may occur automatically. While the term “frictionless” isused for purposes of simplicity, it is to be understood that thisencompasses semi-frictionless checkouts as well. Accordingly, varioustypes of checkout experiences may be considered “frictionless,” and thepresent disclosure is not limited to any particular form or degree offrictionless checkout.

The above described embodiments allow for the tracking of productsselected by a shopper and determining into which shopping receptacleeach selected product is placed. In some scenarios, it might bedesirable to associate different shopping receptacles with differentshopping accounts. For example, a grocery delivery and pick-up servicecompany may allow online customers to order groceries from retailstores, with the shopping being performed by personal shoppers. When ashopper shops for products in a retail store, it might be more efficientfor the shopper to shop for multiple customers simultaneously, eachcustomer having ordered a list of items from the same retail store.Thus, the shopper may carry multiple shopping receptacles for collectingitems to be delivered to the multiple different customers. As usedherein, a shopping receptacle may refer to any container that containsproducts that were picked up by a shopper and placed therein. Theshopping receptacle may be associated with a virtual shopping cart of ashopping account. The shopping receptacle may be a box, a bag, a basket,or a physical shopping cart. After the shopper has collected all of theitems ordered by the multiple customers, the shopper may proceed withthe frictionless checkout described above. According to an embodiment ofthe present disclosure, a system may receive images of the multipleshopping receptacles as well as the products disposed therein. Then, thesystem may analyze received image data to detect product selections andupdate different virtual carts based on which shopping receptacle eachselected product is placed.

FIG. 36 is an illustration of an exemplary system 3600 for frictionlessshopping for multiple shopping accounts, consistent with someembodiments of the present disclosure. As illustrated in FIG. 36 ,system 3600 may include a server 3601, a database 3602, a network 3605,and a plurality of personal devices 3606 (e.g., 3606A, 3606B)respectively associated with a plurality of customers 3608 (e.g., 3608A,3608B). In addition, system 3600 may include one or more physicalshopping carts 3615 in a retail store 3610. Each physical shopping cart3615 may have one or more of a user interface device 3613 and an imagesensor 3614 attached thereto.

In the embodiment illustrated in FIG. 36 , server 3601 may be acloud-based server that communicates with user interface devices 3613,image sensors 3614, and the personal devices 3606A and 3606B via network3605. In some other embodiments, server 3601 may be part of a systemassociated with retail store 3610 that communicates with user interfacedevices 3613 and image sensors 3614 using a wireless local area network(WLAN). According to an embodiment of the present disclosure, sever 3601may receive multiple orders from multiple customers, each orderincluding a list of products to be shopped for in a retail store. Server3601 may also receive image data showing multiple shopping receptaclescarried by a personal shopper in the retail store. Server 3601 mayanalyze the received image data to determine multiple virtual shoppingcarts respectively associated with the multiple shopping receptacles,and to identify the products placed in each one of the multiple shoppingreceptacles. Server 3601 may update the multiple virtual shopping cartsaccording to the identified products.

Server 3601 may be coupled to or communicatively connected to one ormore physical or virtual storage devices such as database 3602. Theinformation stored in database 3602 may be accessed by server 3601 toperform various methods in the embodiments of the present disclosure.Database 3602 may include product type model data (e.g., an imagerepresentation, a list of features, a model obtained by training machinelearning algorithm using training examples, an artificial neuralnetwork, and more) that may be used to identify products that are placedin shopping receptacles in received images. Database 3602 may alsoinclude catalog data (e.g., retail store chain's catalog, retail store'smaster file, etc.) that may be used to check the names and prices of theidentified products. Database 3602 may further include variousinformation about shopping receptacles that may be used to identify theshopping receptacles as well as virtual shopping carts respectivelyassociated with the shopping receptacles. For example, database 3602 maystore correlation information between shopping receptacles and virtualshopping carts. Moreover, database 3602 may include store layouts ofvarious retail stores. Each store layout may include a floor planshowing an arrangement of a plurality of store shelfs within acorresponding retail store, as well as placement of a plurality ofproducts on the store shelfs within the retail store.

Personal devices 3606 (e.g., 3606A, 3606B) may be respectivelyassociated with customers 3608 (e.g., 3608A, 3608B). Each personaldevice 3606 may be configured to present an online shopping platform toa corresponding customer 3608, which allows customer 3608 to orderproducts from retail store 3610. After customers 3608 submit orders,personal devices 3606 may transmit the orders to sever 3601 via network3605. Each order may contain a list to products to be shopped fromretail store 3610, and a time that the products need to be delivered tothe corresponding customer 3608. Based on the received orders, server3601 may generate one or more shopping lists each containing a list ofproducts that need to be collected by a shopper 3612 from retail store3610 for a corresponding customer. Additionally, server 3601 maygenerate a shopping path for shopper 3612 to concurrently collect theproducts in the one or more shopping lists.

Image sensor 3614 may be mounted on physical shopping cart 3615 or maydisposed in various locations in retail store 3700 or 3750 to capturestatic or moving images of various locations in retail store 3700 or3750. Image sensor 3614 may transmit the captured images to server 3601via network 3605. Server 2401 may execute an image analysis process toidentify shoppers as well as products and/or shopping receptacles in thecaptured images, and interactions between the shoppers and the productsand/or bulk packages. For example, server 3601 may detect, based on thecaptured images, that a shopper has placed a first product into a firstshopping receptacle, and placed a second product into a second shoppingreceptacle. Server 3601 may perform the detection based on a movement ofthe shopper in the moving images captured by image sensor 3614.Alternatively or additionally, server 3601 may perform the detection bycomparing two static images of the shopping receptacles taken atdifferent times, and detect any change in the products placed in theshopping receptacles.

User interface device 3613 may communicate with server 3601 to presentinformation derived by server 3601 based on processing of image dataacquired by image sensor 3614. For example, user interface device 3614may present one or more shopping lists each including a list of productsto be shopped for a virtual shopping cart corresponding to an onlineshopping account for a customer. User interface device 3614 may alsopresent one or more virtual shopping carts each including a list ofproducts and a number of these products that have been placed in acorresponding shopping receptacle. An example of the virtual shoppingcart presented in user interface device 3613 is illustrated in FIG. 11E.User interface device 3614 may further present a map, a text message, ora voice message describing the shopping path for shopper 3612 in orderto collect the products in the one or more shopping lists. Moreover,user interface device 3614 may include a text message notifying theshopper if a product that has been placed in a shopping receptacle doesnot belong to the shopping list corresponding to the shoppingreceptacle. User interface device 3614 may be all possible types ofdevices capable of outputting the information derived by server 3601 toshopper 3612, such as a mobile device, a tablet, a personal digitalassistant (PDA), etc.

FIG. 37A is a schematic illustration of an example configuration of aretail store 3700, consistent with an embodiment of the presentdisclosure. As shown in FIG. 37A, retail store 3700 may include aphysical shopping cart 3715. A user interface device 3713 and an imagesensor 3714 may be fixedly attached to physical shopping cart 3715. Userinterface device 3713 may be arranged to display or present information(e.g., shopping list, shopping path, notification, etc.) to shopper 3710when shopper 3710 is holding shopping cart 3715. Image sensor 3714 maybe arranged to capture images of all of the contents in shopping cart3715.

A plurality of shopping receptacles may be disposed in shopping cart3715. The shopping receptacles may include a plurality of boxes, bags,or baskets. In the embodiment illustrated in FIG. 37A, a first box 3720and a second box 3730 may be disposed in shopping cart 3715. Each one offirst box 3720 and second box 3730 may include a plurality of products3722 or 3732 that have been collected by shopper 3710. First box 3720may include a label 3721, which contains a visual identifier (e.g., abarcode, a quick response (QR) code, a flag, a color, an alphanumerictext or code) that uniquely identifies first box 3720. Based on thevisual identifier, a server (e.g., sever 3601) may identify a virtualshopping cart that correlates to first box 3720. Similarly, second box3730 may include a label 3731 which contains a visual identifies thatuniquely identifies second box 3730. In an alternative embodiment, eachone of labels 3721 and 3731 may be a radio-frequency identification(RFID) tag that transmits signals containing identification informationof the corresponding box 3720 or 3730.

In the embodiment illustrated in FIG. 37A, shopping cart 3715 isillustrated as a shopping platform with only one side. In otherembodiments, shopping cart 3715 may include four sides surrounding aplatform. In addition, in the embodiment illustrated in FIG. 37A, firstbox 3720 and second box 3730 are placed in the same shopping cart 3715.In other embodiments, first box 3720 and second box 3730 may be placedin different shopping carts. Still alternatively, shopper 3710 may carrytwo physical shopping carts, each physical shopping cart beingassociated with a virtual shopping cart. In this case, each physicalshopping cart may include a visual identifier or an RFID tag that uniqueidentifies the physical shopping cart. Still alternatively, a box (or abag, a basket, etc.) may be disposed in a physical shopping cart, withthe box being associated with a first virtual shopping cart, and thephysical shopping cart being associated with a second virtual shoppingcart.

FIG. 37B is a schematic illustration of an example configuration of aretail store 3750, consistent with another embodiment of the presentdisclosure. As shown in FIG. 37A, retail store 3700 may include ashopping cart 3715, and an image sensor 3764 mounted on a store shelf3760. Image sensor 3764 may be arranged to capture images of all of thecontents in shopping cart 3715 carried by shopper 3710. A mobile phone3706 associated with shopper 3710 to display or present information(e.g., shopping list, shopping path, notification, etc.) to shopper3710.

FIGS. 38A, 38B, and 38C include flowcharts representing an exemplarymethod 3800 for automatically updating a plurality of virtual shoppingcarts, consistent with an embodiment of the present disclosure. Method3800 may be performed by a processor at a server (e.g., server 135 or2401) or a computer (e.g., one of devices 145A, 145B, 145C, and 145D).It will be appreciated, however, that other implementations are possibleand that other configurations may be used to implement method 3800. Itwill also be readily appreciated that the illustrated method can bealtered to modify the order of steps, delete steps, or further includeadditional steps.

For example, method 3800 may be performed by at least one processingdevice of a server, such as processing device 302, as illustrated inFIG. 3 . In some embodiments, some or all of method 3800 may beperformed by a different device associated with system 100. In someembodiments, a non-transitory computer readable medium may containinstructions that when executed by a processor cause the processor toperform method 3800. Further, method 3800 is not necessarily limited tothe steps shown in FIG. 38A, and any steps or processes of the variousembodiments described throughout the present disclosure may also beincluded in method 3800.

Referring to FIG. 38A, at step 3802, the processor at the server or thecomputer may receive image data captured in a retail store, wherein afirst shopping receptacle and a second shopping receptacle arerepresented in the received image data. In some embodiments, each one ofthe first shopping receptacle and the second shopping receptacle eachmay include at least one of a box, a bag, or a basket. The firstshopping receptacle and the second shopping receptacle may be placed ina single physical shopping cart. For example, as illustrated in FIG.37A, the first shopping receptacle and the second shopping receptaclemay be first box 3720 and second box 3730 that are placed in physicalshopping cart 3715. In some embodiments, at least one of the firstshopping receptacle or the second shopping receptacle is a physicalshopping cart. For example, the first shopping receptacle is a firstphysical shopping cart, and the second shopping receptacle is a secondphysical shopping cart. In some embodiments, the first shoppingreceptacle is a physical shopping cart, and the second shoppingreceptacle comprises at least one of a box, a bag, or a basket. In someembodiments, the image data may include images captured by at least oneimage sensor fixedly mounted to a physical shopping cart in the retailstore. For example, as illustrated in FIG. 37A, image sensor 3714 may befixedly mounted to physical shopping cart 3715 in retail store 3700. Insome embodiments, the image data may images captured by at least oneimage sensor fixedly mounted to a store shelf in the retail store. Forexample, as illustrated in FIG. 37B, image sensor 3764 may be mounted tostore shelf 3760 in retail store 3750.

At step 3804, the processor may determine that the first shoppingreceptacle is associated with a first virtual shopping cart and that thesecond shopping receptacle is associated with a second virtual shoppingcart different from the first virtual shopping cart. In someembodiments, the processor may perform the determination in step 3804based on analysis of the received image data. For example, each of thefirst shopping receptacle and the second shopping receptacle may includea visual identifier that correlates a shopping receptacle to a virtualshopping cart. The visual identifier may include a barcode, a quickresponse (QR) code, a flag, colors, an alphanumeric text or code, etc.For example, as illustrated in FIGS. 37A and 37B, each one of boxes 3720and 3730 placed on shopping cart 3715 may include a label 3721 or 3731that has the barcode, the QR code, the flag, the color, or thealphanumeric text or code. In some embodiments, the processor mayperform the determination in step 3804 based on one or more electronicsignals. The one or more electronic signals may be received fromradio-frequency identification (RFID) tags associated with each of thefirst shopping receptacle and the second shopping receptacle. The RFIDtags may transmit signals include identification information of theshopping receptacles.

In some embodiments, the correlation between the shopping receptaclesand the virtual shopping carts may be established when a shopper entersa retail store. For example, as illustrated in FIG. 37A, shopper 3710may manually enter, via user interface device 3713 mounted on shoppingcart 3715, identification information (e.g., an alphanumerical code) ofeach of first box 3720 and second box 3730, and identificationinformation (e.g., account number) of each one of the first virtualshopping cart and the second virtual shopping cart. User interfacedevice 3713 may then transmit the correlation information between theboxes and the virtual shopping carts to a database. For another example,as illustrated in FIG. 37B, shopper 3710 may use mobile phone 3706 toscan first and labels 3721 and 3731 on first and second boxes 3720 and3730 to acquire their identification information. Then, shopper 3710 mayuse mobile phone 3706 to select a virtual shopping account to beassociated with each one of first and second boxes 3720 and 3730. Mobilephone 3706 may then transmit the correlation information between theboxes and the virtual shopping carts to a database. In some embodiments,after detecting that the shopper has placed a product into a shoppingreceptacle and that the correlation between the shopping receptacles andthe virtual shopping cart has not been established, the process maytransmit a notice to the shopper via, for example, user interface device3713 or mobile phone 3706, to remind the shopper to establish suchcorrelation.

At step 3806, the processor may analyze the received image data todetect a shopper placing a first product in the first shoppingreceptacle and to detect the shopper placing a second product in thesecond shopping receptacle. For example, as illustrated in FIGS. 37A and37B, the processor may analyze the image data to detect that shopper3710 places first product 3722 in first box 3720, and places secondproduct 3732 in second box 3730. In some embodiments, in step 3806, theprocessor may analyze the received image data to first detect anexistence of each one of first product 3722 and second product 3732, andthen identify first product 3722 and second product 3732. For example,for each one of first product 3722 and second product 3732, theprocessor may determine a unique identifier associated with the productand then retrieve product information (e.g., product type, prices, etc.)from a product catalogue stored in a database (e.g., database 3602 or alocal database in retail store 3700 or 3750) according to the uniqueidentifier. In some examples, the image data may be analyzed todetermine positions of first product 3722 and second product 3732 (forexample, in the image data, in relation to a physical object, inreal-world coordinates), for example using visual object detectionalgorithms. Further, the image data may be analyzed to determinepositions of the first shopping receptacle and the second shoppingreceptacle (for example, in the image data, in relation to a physicalobject, in real-world coordinates), for example using visual objectdetection algorithms. Further, step 3806 may detect that the shopperplaced the first product in the first shopping receptacle and that theshopper placed the second product in the second shopping receptaclebased on the determined positions of the first product, the firstshopping receptacle, the second product and the second shoppingreceptacle, for example based on proximity between the determinedposition of the first product and the determined position of the firstshopping receptacle and/or based on proximity between the determinedposition of the second product and the determined position of the secondshopping receptacle. In some examples, the image data may be analyzed todetermine a relative motion between the first product and the firstshopping receptacle, and/or to determine a relative motion between thesecond product and the second shopping receptacle, for example usingvisual motion detection algorithms. Further, step 3806 may detect thatthe shopper placed the first product in the first shopping receptacleand that the shopper placed the second product in the second shoppingreceptacle based on the determined relative motion between the firstproduct and the first shopping receptacle and/or on the determinerelative motion between the second product and the second shoppingreceptacle, for example, based on the first product moving towards thefirst shopping receptacle and based on the second product moving towardsthe second shopping receptacle. In some examples, a convolution of atleast part of the image data may be calculated. Further, in response toa first value of the calculated convolution of the at least part of theimage data, step 3806 may detect that the shopper placed a first productin the first shopping receptacle, and in response to a second firstvalue of the calculated convolution of the at least part of the imagedata, step 3806 may forgo detecting that the shopper placed a firstproduct in the first shopping receptacle.

At step 3810, in response to detecting that the shopper placed the firstproduct in the first shopping receptacle, the processor mayautomatically update the first virtual shopping cart to includeinformation associated with the first product. In some embodiments, theprocessor may automatically update the first virtual shopping cart byadding a price of the first product to an invoice associated with thefirst virtual shopping cart. The processor may also add a product type,a product name, and a quantity of the first product placed in the firstshopping receptacle, to the invoice associated with the first virtualshopping cart.

At step 3812, in response to detecting that the shopper placed thesecond product in the second shopping receptacle, the process mayautomatically update the second virtual shopping cart to includeinformation associated with the second product. In some embodiment, theprocessor may automatically update the second virtual shopping cart byadding a price of the second product to an invoice associated with thesecond virtual shopping cart. The processor may also add a product type,a product name, and a quantity of the second product placed in thesecond shopping receptacle, to the invoice associated with the secondvirtual shopping cart.

Referring to FIG. 38B, method 3800 for automatically updating thevirtual shopping carts may further includes steps 3822, 3824, and 3826.In some embodiments, one or more of steps 3822, 3824, and 3826 may beperformed before the steps in FIG. 37A.

At step 3822, the processor may receive a first shopping list associatedwith the first virtual shopping cart and a second shopping listassociated with the second virtual shopping cart. Each shopping list mayinclude a list of products that need to be purchased by the shopper forthe corresponding virtual shopping cart.

At step 3824, the processor may determine a path for the shopper tocollect products from the first shopping list for placement in the firstshopping receptacle and to concurrently collect products from secondshopping list for placement in the second shopping receptacle. Theprocessor may determine the path based on both of the first shoppinglist and the second shopping list, as well as a store layout of theretail store. The store layout may include a floor plan showingarrangement of a plurality of store shelfs within the retail store, aswell as placement of a plurality of products on the store shelfs. Thestore layout may be stored in a database (e.g., database 3602 or a localdatabase in retail store 3700 or 3750). In another example, the storelayout may be on an analysis of image data captured from the retailstore. For example, locations of products of different product types maybe determined based on the analysis of the image data as describedherein.

At step 3826, the processor may deliver at least one indication of thedetermined path to the shopper. For example, the processor may deliverthe indication of the determined path to user interface device 3713 ofFIG. 37A, or to mobile phone 3706 associated with shopper 3710 in FIG.37B. The indication of the determined path may include a diagrammaticrepresentation of the determined path overlaid on top of a floor plan ofthe retail store. Alternatively or additionally, the indication of thedetermined path may include a list of aisle numbers, shelf numbers, andproducts names sequentially arranged in an order determined by theprocessor.

Referring to FIG. 38C, method 3800 for automatically updating thevirtual shopping carts may further include steps 3842 through 3848.

At step 3842, the processor may receive a first shopping list associatedwith the first virtual shopping cart and a second shopping listassociated with the second virtual shopping cart.

At step 3844, the processor may analyze the received image data todetermine whether products that the shopper places into the firstshopping receptacle are included on the first shopping list. Theprocessor may perform the analysis every time the shopper places a newproduct into the first shopping receptacle. Alternatively, the processormay perform the analysis at a predetermined interval, for example, every5 minutes, every 10 minutes, every 20 minutes, etc. If the processorperforms the analysis at the predetermined interval, the processor mayonly need to compare the products placed into the shopping receptacleswith the shopping list within the predetermined interval. Stillalternatively, the processor may perform the analysis before a checkoutprocess. In some examples, a convolution of at least part of thereceived image data may be calculated, in response to a first value ofthe calculated convolution, step 3844 may determine that products thatthe shopper places into the first shopping receptacle are included onthe first shopping list, and in response to a second value of thecalculated convolution, step 3844 may determine that at least oneproduct that the shopper places into the first shopping receptacle isnot included on the first shopping list. In some examples, the receivedimage data may be analyzed using visual product recognition algorithm toidentify types and/or quantities of the products that the shopper placesinto the first shopping receptacle, and step 3844 may compare theidentified types and/or quantities with product types and/or quantitiesin the first shopping list to determine whether the products that theshopper places into the first shopping receptacle are included on thefirst shopping list.

At step 3846, if the processor determines that a particular product thatthe shopper places into the first shopping receptacle is not on thefirst shopping list, the processor may automatically provide anotification to the shopper indicating that the particular product noton the first shopping list has been placed into the first shoppingreceptacle. The notification may include an identification of theparticular product. In some embodiments, the notification may beprovided to mobile phone 3706 associated with shopper 3710 (asillustrated in FIG. 37B). Alternatively or additionally, thenotification may be provide to user interface device 3713 mounted onphysical shopping cart 3715 (as illustrated in FIG. 37A).

At step 3848, the processor may further determine if the particularproduct that the shopper places into the first shopping receptacle butis not on the first shopping list, is on the second shopping listassociated with the second virtual shopping cart. If the processordetermines that the particular product is on the second shopping list,the processor may include in the notification to the shopper anindication that a proper placement of the particular product was in thesecond shopping receptacle. If the processor determines that theparticular product is not on the second shopping list, the processor mayforgo including the indication that the proper placement of theparticular product was in the second shopping receptacle.

As described above, the disclosed embodiments may allow foridentification and tracking of products selected by shoppers in a retailenvironment by processing image data or other sensor data. In someembodiments, this may allow for a frictionless shopping experience for ashopper, as described above. In some instances, the captured image datamay be insufficient for fully identifying a selected product. Forexample, ambiguity may exist regarding which product a shopper selected.To aid in resolving such ambiguous product selection events, thedisclosed embodiments may include accessing an electronic shopping listassociated with a customer. For example, if it is unclear based on imagedata whether a shopper has selected Pepsi® or Diet Pepsi®, and anelectronic shopping list associated with the shopper includes DietPepsi®, it may be more likely that the shopper has selected the item onthe shopping list. This may also assist in preserving a shopper'seligibility for frictionless checkout as described above with respect toFIGS. 18-20B.

As used herein an electronic shopping list may refer to any datarepresenting items that are associated with a customer. In someembodiments, the shopping list may be a list of desired items. Forexample, a customer of a retail store may create a shopping list ofitems that he or she intends to purchase from the retail store.Alternatively or additionally, the electronic shopping list may be alist of items associated with the customer based on previous purchases.For example, the electronic shopping list may include a list of allitems a customer has ever purchased, a list of items most commonlypurchase, a list of items purchased in one or more previous visits, orthe like. Accordingly, the list may be automatically generated andmaintained by a server or other computing device.

The electronic shopping list may be generated and/or stored in anysuitable format for representing a list of products. For example, theelectronic shopping list may be stored as a list, an array, a textstring or text file, a table, a database, or various other datastructures. In some embodiments the electronic shopping list may bestored in local memory of a device. For example, a shopping list may bestored in a phone or other mobile device of a customer, shopper, storeassociate, or the like. Alternatively or additionally, the electronicshopping device may be stored remotely and may be accessed by a devicewithin the retail store. For example, the electronic shopping list maybe stored on a remote server (e.g., server 135), on a cloud storageplatform, on a web server, on remote desktop or laptop computing device,or any other storage device accessible via a network.

FIG. 39 illustrates an example electronic shopping list 3900 associatedwith a customer, consistent with the disclosed embodiments. In thisexample, electronic shopping list 3900 may be displayed on a mobiledevice 3910. In some embodiments, mobile device 3910 may be associatedwith the customer. For example, the customer may access and referenceelectronic shopping list 3900 while shopping in a retail store. Asanother example, mobile device 3910 may be associated with a shopper,which may be a different entity than the customer. For example, ashopper may act as a proxy for the customer and may shop on thecustomer's behalf using the electronic shopping list. The shopper may bea human shopper or may be a robotic device configured to select productsfrom one or more retail shelves. In some embodiments, mobile device 3910may correspond to various other devices described herein, including oneor more of output devices 145A, 145B, 145C and 145D. Electronic shoppinglist 3900 may be accessible to other devices, such as image processingunit 130, which may use electronic shopping list 3900 to resolveambiguous product selection events, as described herein. While FIG. 39shows electronic shopping list 3900 being displayed on mobile device3910, it is to be understood that electronic shopping list 3900 may notnecessarily be displayed in the retail environment and may be stored andaccessed electronically.

As shown in FIG. 39 , electronic shopping list 3900 may include one ormore products, such as products 3902, 3904, and 3906. Electronicshopping list 3900 may include additional information describing eachproduct in the list. For example, electronic shopping list 3900 mayinclude one or more of a brand name, a model, a product type or subtype(e.g., a product flavor, diet vs. regular, whole vs. 2% milk, etc.), aproduct category, a unit or packaging type (e.g., can, bottle, jar, box,etc.), a product size, or any other information that may help toidentify a particular product. In some embodiments, electronic shoppinglist 3900 may include a product number, such as a stock-keeping unit(SKU) number or other identifier. In some embodiments, the product maybe represented in electronic shopping list 3900 by the identifier andthe information displayed in FIG. 39 may be accessed through a look-upfunction based on the identifier.

In some embodiments, electronic shopping list 3900 may include otherdata, such as a quantity of items to be purchased. For example, product3904 may be associated with a quantity of 2 units, whereas product 3902may be associated with a quantity of 3 units, as shown. As anotherexample, electronic shopping list 3900 may include data or metadataindicating whether a product has been selected by a shopper, whether aproduct has been purchased, whether a product is in stock, or variousother information associated with the product, which may or may not bedisplayed on mobile device 3910. In other words, mobile device 3910 mayonly display a subset or summary of information included in electronicshopping list 3900. In some embodiments, mobile device 3910 may includea checkbox or other element indicating whether a product has beenselected, purchased, or the like. For example, mobile device 3910 maydisplay checkbox 3912 indicating whether product 3904 has been selectedby the shopper. Checkbox 3912 may be interactive such that a shopper mayselect checkbox 3912 to toggle a status associated with product 3904.

Electronic shopping list 3900 may be generated in various ways. In someembodiments, electronic shopping list 3900 may be generated by acustomer associated with electronic shopping list 3900. For example, acustomer may select products for inclusion in electronic shopping list3900 from a larger list of available products via user interface. Forexample, the user interface may be presented through an app orapplication (e.g., a retail store app, a grocery or other productdelivery app, a generic shopping list app, or the like). In someembodiments, this may include a web-based application, such as a websiteor other online interface for a retailer, a delivery service, or thelike. The customer may select from a list of all available products inthe store to build electronic shopping list 3900. The customer mayselect the products through mobile device 3910 or through anotherdevice, such as a personal computing device, a second mobile device, atablet, a laptop, or the like.

Alternatively or additionally, electronic shopping list 3900 may begenerated automatically. In some embodiments, electronic shopping list3900 may be at least partially generated based on a customer's shoppinghistory in a particular retail store. The automatically generatedshopping list may be a prediction of items the customer would like topurchase during the next visit. For example, if a certain customeralways buys a particular brand of organic ketchup, electronic shoppinglist 3900 may be generated to include this product. When an ambiguityarises regarding which ketchup product a customer selects, the disclosedembodiments may include accessing electronic shopping list 3900 todetermine which type of ketchup the customer usually purchases. In someembodiments, a customer may confirm, modify, supplement, or revise thelist of automatically generated products included in electronic shoppinglist 3900. Alternatively or additionally, electronic shopping list 3900may not be tied to a particular visit but may be a record of previouspurchases, as described above.

As described herein, the disclosed systems and methods may detect itemsselected by a shopper using image data. Items identified as having beenselected may be included in a virtual shopping cart associated with ashopper (and/or a customer). Accordingly, in this context, a virtualshopping cart may refer to a list of items having been selected in aretail store by a shopper. This virtual shopping cart may be used duringa checkout process by the shopper. For example, the disclosed systemsmay generate a total amount due based on the items in the virtualshopping cart and request payment from the shopper or customer for theitems. In some embodiments, the virtual shopping cart may be africtionless checkout as described throughout the present disclosure.Accordingly, it may be beneficial to maintain an accurate virtualshopping cart for a shopper to avoid potential issues during checkout orto maintain frictionless checkout eligibility for a shopper.

As described above, various forms of ambiguity may arise when a shopperselects a product. FIG. 40A illustrates an example product interactionevent 4000 that may be detected, consistent with the disclosedembodiments. As shown in FIG. 40A, a shopper 4020 may interact with aproduct 4010 in a retail environment, which may include, looking atproduct 4010, stopping in front of product 4010, picking up product 4010from a shelf 4002, returning product 4010 to shelf 4002, placing product4010 in a shopping cart 4022 associated with shopper 4020, or variousother forms of interaction. In some embodiments, the interaction betweenshopper 4020 and product 4010 may at least partially be detected by asensor. For example, the sensor may include a camera 4030, as shown inFIG. 40A. Camera 4030 may include any device capable of capturing one ormore images from within a retail environment. In some embodiments,camera 4030 may correspond to image capture device 125 (includingdevices 125A, 125B, 125C, 125D, 125E, 125F, or 125G) as described above.Accordingly, any embodiments or features described in reference to imagecapture device 125 may equally apply to camera 4030. Camera 4030 (and insome cases, additional image capture devices) may be used to identifyshopper 4020 in the retail environment, as well as product 4010, asdescribed above.

In some embodiments, product interaction event 4000 may be an ambiguousproduct selection event due to a view of camera 4030 being at leastpartially blocked. For example, shopper 4020 may be positioned such thatthe interaction with product 4010 by shopper 4020 is blocked by shopper4020 or an obstacle, such as another shopper, a shelf, another shoppingcart, or the like. Accordingly, it may be unclear whether the productwas selected or returned to the shelf. For example, it may be unclearwhether product 4010 was selected by shopper 4020 (e.g., placed intoshopping cart 4022) or was returned to shelf 4002. Or, if a product wasselected, it may also be unclear whether product 4010 was selected orwhether a different product from shelf 4002 was selected. Various othertypes of events related to camera 4030 may cause uncertainty as to theinteraction with product 4010, as described above with respect to FIG.18 . It is to be understood that the ambiguous product interaction eventillustrated in FIG. 40A is provided by way of example, and various otherscenarios may lead to uncertainty in the selection of products.

In some embodiments, the shopper may be associated with multipleelectronic shopping lists. For example, as noted above, the shopper maybe a “picker” or proxy shopper who may shop for multiple customers, eachbeing associated with a different electronic shopping list. As anotherexample, the shopper may be a robotic device programmed to selectproducts for different customers. Accordingly, identifying an electronicshopping list may include selecting an electronic shopping list from aplurality of electronic shopping lists associated with a shopper. Insome embodiments, server 135 may select the electronic shopping listbased on image data. For example, this may include detecting productselection events, and comparing the selected products to each of theplurality of electronic shopping lists. In some embodiments, theidentification of the electronic shopping list may be based on areceptacle corresponding to a detected product selection event. Forexample, the shopper may have a cart or other apparatus for carryingproducts having multiple receptacles associated with different customers(or different electronic shopping lists). Based on which receptacle theselected product is placed into, the associated electronic shopping listmay be identified. These receptacles may include shopping bags, bins,separate carts, boxes, cartons, or other forms of containers.

Consistent with the embodiments disclosed herein, server 135 may accessan electronic shopping list to resolve the ambiguity. FIG. 40B is adiagrammatic illustration of an example process for resolving anambiguity based on a shopping list, consistent with the disclosedembodiments. Based on product interaction event 4000, server 135 mayaccess electronic shopping list 3900, as shown in FIG. 40B. Based on thecontents of electronic shopping list 3900, which may be associated withshopper 4020, sever 135 may make an identification 4040 of product 4010.For example, server 135 may determine that product 4010 is a condimentbased on the location of shopper 4020 and/or product 4010, but may notknow which condiment has been selected. In some embodiments, server 135may determine that product 4010 is a bottle of ketchup but server 135may be missing further information to more precisely identify product4010. Using the example of electronic shopping list 3900 shown in FIG.39 , server 135 may determine that electronic shopping list 3900includes product 3904, which represents a particular organic ketchupproduct. Accordingly, server 135 may determine that the ketchup product4010 selected by shopper 4020 is most likely product 3904. Accordingly,as shown in FIG. 40B, identification 4040 may include identifyingproduct 4010 as a 20 oz. bottle of organic ketchup as opposednon-organic ketchup, other brands of organic ketchup, other sizes oforganic ketchup, or the like.

Based on identification 4040, product 4010 may be added to virtualshopping cart 4050 associated with shopper 4020. Accordingly, virtualshopping cart 4050 may be used in a checkout process associated with theshopper. In some embodiments, the checkout process may be a frictionlesscheckout process, as described above. Accordingly, virtual shopping cart4050 may be associated with a frictionless checkout. As described above,this may also require a shopper to maintain a frictionless checkouteligibility status, which may be lost due to ambiguous productinteraction events. Accordingly, in response to identification 4040, africtionless shopping eligibility status associated with the shopper maybe maintained. In some embodiments, based on identification 4040,electronic shopping list 3900 may be updated to reflect theidentification of product 4010. For example, this may include marking anitem as having been selected from the list, updating a quantityassociated with the item on the list, removing the item, or the like. Insome embodiments, the updated electronic shopping list 3900 may then beused for additional product selection events. The refined list maynarrow the candidate products for future product selection events, whichmay improve the accuracy for future identifications.

In some embodiments, the disclosed embodiments may include selectingbetween multiple items in electronic shopping list 3900. For example, ifserver 135 determines that product 4010 is a condiment but electronicshopping list 3900 includes multiple condiments, identification 4040 mayinclude selecting between the condiments included in electronic shoppinglist 3900. In some embodiments, this may be based on informationincluded in the captured images. For example, if system 135 determinesthat product 4010 has a size, shape, color, position, and/or otherproperties that are more consistent with product 3904 than othercondiment products on electronic shopping list 3900 (such as product3906), product 4010 may be identified as product 3904. In someembodiments, the quantity of product selected may be compared to aquantity associated with products on electronic shopping list 3900,which may assist in identifying product 4010. For example, if shopper4020 selects two of product 4010, it may be more likely that product4010 is product 3904, consistent with the quantity for product 3904indicated in electronic shopping list 3900.

In some embodiments, identification 4040 may be based on an indicator ofa confidence level, such as a confidence score, associated with theidentification of product 4010. For example, server 135 may generate aconfidence score indicating a degree of likelihood that product 4010 hasbeen correctly identified. The confidence score may be generated basedon various factors, including an image quality of the representation ofproduct 4010 in an image, a degree of matching between detected product4010 and an expected appearance of a product in an electronic shoppinglist, how closely the number of products selected matches the quantityfor a product in the electronic shopping list, an inventory of theidentified product in the retail store, or any other factors that mayaffect a confidence level for identification 4040.

In some embodiments, identification 4040 may be based on a comparison ofa confidence score to a threshold. For example, a minimum confidencescore threshold value may be set and a product may be identified andadded to virtual cart 4050 if a confidence score exceeds the minimumthreshold value. The confidence level for a product identification andthe threshold values may similarly be used in association withmaintaining a frictionless checkout status for a shopper. In someembodiments, the threshold value may vary depending on other factors. Asone example, the predetermined threshold may vary based on a producttype. For example, more expensive items, such as electronics may requirea higher confidence score to be identified or for a shopper to maintainfrictionless checkout eligibility. As another example, the predeterminedthreshold may depend on a location in the retail store. For example,more expensive or critical areas of a store (e.g., a pharmacy, or thelike) may require a higher confidence level. In some embodiments, theconfidence score may be used to distinguish between multiple products onelectronic shopping list 3900. For example, if product 4010 isdetermined to be a condiment, a confidence score may be generated foreach of products 3904 and 3906. The product on electronic shopping list3900 having the highest confidence score may be identified inidentification 4040.

Consistent with the disclosed embodiments, various additionalinformation may be used to identify product 4010. FIG. 41 illustratesexample information that may be used to identify a product or to confirma product identification, consistent with the disclosed embodiments. Asshown in FIG. 41 server 135 may access a data source 4110, which maystore various information associated with a retail store, a customer, aproduct, a product manufacturer, or any other information that may berelevant to identification of a product. Data source 4110 may includeany device or interface through which this data may be accessed. Forexample, data source 4110 may be a database, a server, a local harddrive or memory device, a cloud storage platform, an online resource(e.g., a webpage, a website, etc.), sensor output, electronictransmission, or any other source of information. In some embodiments,data source 4110 may correspond to one or more other data sourcesdescribed throughout the present disclosure, including memory device226, database 140, one or more of devices 145A, 145B, 145C, and 145D, orany other devices associated with system 100.

In some embodiments, server 135 may access inventory data 4120. As usedherein, inventory information may refer to any information indicating aninventory of a retail store. For example, inventory data 4120 mayinclude a database, record, or other data structure indicating a numberof products available within a retail store. This may include a numberof items displayed on retail shelving, a number of items in a storagearea, or both. For example, inventory data 4120 may refer to aninventory of product 3620 included in a storage area of the retail storeincluding shelving unit 3610. The storage area may be a storage room, aportion of a shelving unit within the retail store dedicated to storage(e.g., a top shelf, etc.), or any other suitable storage location. Inother embodiments, inventory data 4120 may refer to off-site storage,for example, in a warehouse, in a delivery truck, or the like.

Inventory data 4120 may be accessed by server 135 and used to identifyproduct 4010. For example, if product 3904 is out of stock, as indicatedby inventory data 4120, product 4010 likely does not correspond toproduct 3904. Similarly, the stocked inventory of products not onelectronic shopping list 3900 may also help identify product 4010. Forexample, if products similar to product 3904 are out of stock, it may bemore likely that product 4010 corresponds to product 3904, which mayincrease a confidence level, etc. In some embodiments, server 135 mayidentify product 4010 as corresponding to a second product on electronicshopping list 3900 when a first product is out of stock. For example, asdescribed above, electronic shopping list 3900 may include a historicallist of items purchased by a shopper or customer. The electronicshopping list may further include ranking information associated withthe customer's past purchases within a particular product type group.This ranking may be based on how commonly each product is purchased, apreference ranking input by the customer, a relative price of theproducts, or any other information that may rank products relative toeach other. In some embodiments, the ranking may be specific toinstances where the first product is out of stock. For example, theranking may be an indication of what products the customer typicallybuys when product 3904 is out of stock. If the customer typically buys adifferent brand or different size of product in this scenario thatreplacement product may be identified as product 4010. The ranking mayalso be a general ranking of how commonly products are purchased (i.e.,not in the context of a primary product being out of stock).

In some embodiments, inventory data 4120 may be used to updateelectronic shopping list 3900. For example, if product 3904 is indicatedas being out of stock, a substitute product may be added to electronicshopping list 3900, based on a similar analysis as described above. Thisupdated electronic shopping list may then be used to resolve productselection ambiguities. For example, when product interaction event 4000is detected, electronic shopping list 3900 may have already been updatedbased on product 3904 being out of stock. In particular, product 3904may be replaced with a substitute product determined to be most likelyto be purchased instead of product 3904. Information about thesubstitute product may then be used to perform identification 4040,consistent with the techniques described above.

As another example of information that may aid in identifying a product,server 135 may access planogram data 4130. As used herein, planogramdata may refer to any information indicating a preferred or intendedplacement of products on a retail shelf. For example, planogram data4130 may include information associated with contractual obligationsand/or other preferences related to the retailer methodology forplacement of products on the store shelves. Based on image data (e.g.,images acquired by camera 4030), server 135 may determine an area 4132(or approximate area) from which product 4010 was selected. Area 4132may be compared to a location within a planogram based on planogram data4130 to identify or help identify product 4010. Combined with theanalysis based on electronic shopping list 3900, planogram data 4130 mayincrease the accuracy of product identification 4040. In someembodiments, this may result in a higher confidence level associatedwith identification 4040.

As another example, server 135 may access product affinity data 4140associated with the customer. As used herein, product affinity data mayrefer to any information indicating a preference for or tendency towarda product or product type by a customer. Product affinity data 4140 maybe specific to a particular customer. For example, product affinity data4140 may be a ranking of historical purchases by a customer, which mayindicate an affinity for a particular product. Affinity data 4140 mayalso indicate an affinity for a particular product type. For example, ifthe customer consistently buys organic products, bulk products, low fator fat-free varieties, or the like, it may indicate an affinity betweenthe customer and this product type. In some embodiments, affinity data4140 may be based on information input by the customer. For example, thecustomer may select or otherwise identify preferred products or producttypes through a user interface, which may be stored as affinity data4140. As another example, a user may “like” or rate particular products,which may indicate an affinity between the customer and those products.Accordingly, affinity data 4140 may be accessed from a social medianetwork or other platform through which a ratings or other indicationsof product affinities may be recorded. Various other types ofinformation, including browsing history, search history, or other dataassociated with a customer may similarly indicate product affinities.

In some embodiments, product affinity data 4140 may not be specific to aparticular customer. Rather, product affinity data 4140 may reflectpreferences or tendencies of other customers. For example, this mayinclude the highest ranked product, most liked product, a trendingproduct, or other indications of affinities between customers andproducts. In some embodiments, product affinity data 4140 may indicate apairwise affinity between products or product types. For example,customers who commonly buy Brand X of deodorant may be more likely tobuy Brand X (or even Brand Y) of shaving cream. As another example,customers who frequently buy organic products may commonly purchasesulfate-free products. These product affinities may be indicated inproduct affinity data 4140. Accordingly, if a shopper has alreadyselected sulfate-free shampoo, it may be more likely that product 4010is organic ketchup rather than regular ketchup. This product affinitydata may be used to further identify product 4010, which may increase aconfidence level associated with product identification 4040.

Various other information accessible to server 135 may also be used toidentify products in conjunction with electronic shopping list 3900. Forexample, this may include analyzing image data to determine a locationof the shopper during product interaction event 4000. This location maybe used to further refine the identification of product 4010. Forexample, if the shopper is in an all-natural or organic aisle, it may bemore likely that product 4010 corresponds to product 3904 as opposed toregular ketchup. As another example, the shopper may be near apromotional display which may narrow or limit the candidates for product4010. It is to be understood that the additional information that may beused to identify product 4010 in conjunction with information fromelectronic shopping list 3900 is not limited to the types of informationshown in FIG. 41 and any type of information described throughout thepresent disclosure may be used. This may include data from othersensors, data associated with other shoppers, detected actions by theshopper or other shoppers, or the like.

FIG. 42 is a flowchart of an exemplary method for using an electronicshopping list to resolve ambiguity associated with a selected product,consistent with the present disclosure. Process 4200 may be performed byat least one processing device of a server, such as processing device302, as described above. In some embodiments, some or all of process4200 may be performed by a different device associated with system 100.In some embodiments, a non-transitory computer readable medium maycontain instructions that when executed by a processor cause theprocessor to perform process 4200. Further, process 4200 is notnecessarily limited to the steps shown in FIG. 42 , and any steps orprocesses of the various embodiments described throughout the presentdisclosure may also be included in process 4200, including thosedescribed above with respect to FIGS. 39, 40A, 40B, and 41 .

In step 4210, process 4200 includes accessing an electronic shoppinglist associated with a customer of a retail store. For example, this mayinclude accessing electronic shopping list 3900 described above. Theelectronic shopping list may be stored in any location or plurality oflocations accessible to server 135. For example, accessing theelectronic shopping list may include accessing the electronic shoppinglist from a server, from a cloud storage platform, from a website orother online or web-based platform, a local device memory, or any otherstorage location. The electronic shopping list may be generated invarious ways. In some embodiments, the electronic shopping list may begenerated by the customer. For example, the customer may select one ormore products for inclusion in the electronic shopping list through auser interface. Alternatively or additionally, the electronic shoppinglist may be automatically generated. For example, the electronicshopping list may be generated based on the customer's shopping historyin the retail store. Accordingly, the electronic shopping list may be ahistorical list of items purchased by the customer, which may includethe quantity or frequency of items being purchased, the time or date ofpurchase, or other information.

In step 4220, process 4200 includes receiving image data captured usingone or more image sensors in the retail store. For example, this mayinclude receiving image data from camera 4030. The image sensors may bepositioned such that product interaction events may be detected usingthe image data. The image data may be processed using an imageprocessing unit 130 as described above.

In step 4230, process 4200 includes analyzing the image data to detect aproduct selection event involving a shopper. For example, this mayinclude detecting product selection event 4000, as shown in FIG. 40A. Insome embodiments, the product selection event may be an ambiguousproduct selection event. In other words, at least one aspect of theselected product may not be clear or fully identified based on analysisof the image data alone. In some embodiments, the shopper may also bethe customer of the retail store of step 4210. Alternatively oradditionally, the shopper may be a proxy for the customer and may shopfor the customer based on the electronic shopping list. For example, theshopper may be a “picker” or other individual tasked with shopping forone or more customers. Some other non-limiting examples of suchambiguous product selection events are described above, for example, inrelation to FIGS. 18-20 . In some embodiments, the shopper may be arobot.

In some embodiments, the shopper may be associated with a plurality ofdifferent electronic shopping lists. The different electronic shoppinglists may correspond to different customers. For example, the shoppermay select or pick products for multiple customers at the same time orat different times. Alternatively or additionally, the electronicshopping lists may correspond to the same customer. For example, theshopper may be the customer, but the customer may have multipleelectronic shopping lists. Accordingly, process 4200 may further includeanalyzing the image data to select the electronic shopping list from theplurality of different electronic shopping lists, where the electronicshopping list corresponds to the detected product selection event. Forexample, the selection of the electronic shopping list may be based on areceptacle corresponding to the detected product selection event, asdescribed above.

In step 4240, process 4200 includes identifying a product associatedwith the detected product selection event based on analysis of the imagedata and further based on the electronic shopping list. For example,based on the analysis of the image data, at least one characteristic ofthe selected product may be determined. Step 4240 may include comparingthe at least one characteristic of the selected product to one or morecharacteristics of products included in the electronic shopping list.Based on a match between the determined at least one characteristic ofthe selected product with at least one characteristic of a productincluded in the electronic shopping list, the selected product may beidentified. Referring to the example from FIG. 40B, the selected productmay be identified as a ketchup bottle based on analysis of the imagedata, but it may not be clear exactly which ketchup product wasselected. Step 4240 may include referencing the electronic shopping listto identify ketchup products included in the electronic shopping list.If the list includes a 20 oz. bottle of organic ketchup of a particularbrand (e.g., product 3904), this information may be used to furtheridentify the selected product.

Consistent with various embodiments of the present disclosure, step 4240may include accessing additional information for identifying the productassociated with the detected product selection event. For example, thismay include accessing data from data source 4110, as described above. Insome embodiments step 4240 may include accessing inventory informationassociated with the retail store, such as inventory data 4120. Theidentification of the product may further be based on the inventoryinformation. For example, if a particular product is indicated as beingout of stock, it may be likely that the selected product is not theparticular product that is out of stock. In some embodiments, theelectronic shopping list may include ranking information associated withthe customer's past purchases of products of a particular product typegroup. In the event that a product listed on the electronic shoppinglist is indicated as being out of stock, step 4240 may includeidentifying the selected product based on the ranking information. Forexample, if when Pepsi Zero Sugar® is not available, the shopper orcustomer typically chooses Diet Pepsi®, when Pepsi Zero Sugar® isindicated as being out of stock, the selected product may be identifiedas Diet Pepsi® in step 4240. In some embodiments, process 4200 mayinclude updating the electronic shopping list based on the inventoryinformation. For example, the shopping list may be compared to theinventory information and may be modified to include substitutes for anyitems not in stock. For example, if Pepsi Zero Sugar® is not in stock,the shopping list may be automatically updated to include Diet Pepsi®.Accordingly, step 4240 may include basing the identification of theproduct on the updated electronic shopping list.

In some embodiments, step 4240 may include accessing planograminformation indicative of a desired placement of products on shelves ofthe retail store. For example, this may include accessing planogram data4130, as shown in FIG. 42 . Accordingly, as described above, step 4240may include further basing the identification of the product on theplanogram information. For example, an arca or approximate arca theproduct was selected from may be compared to the planogram informationto further identify the selected product.

As another example, step 4240 may include receiving product affinityinformation associated with the customer. For example, this may includeproduct affinity data 4140, as shown in FIG. 42 . As described above,the product affinity information may include indications of affinitiesbetween the customer and a product or product type. Alternatively oradditionally, this may include pairwise affinities between products orproduct types. For example, the product affinity information mayindicate a correlation between purchasing products of a first type andproducts of a second type by the same customers. The product affinityinformation may be specific to the customer (e.g., based on transactionhistory for the customer) or may be based on an aggregation oftransaction history for a plurality of customers. The plurality ofcustomers may be selected in various ways. For example, the plurality ofcustomers may be customers of the same retail store, customers who havepurchased a particular product (e.g., a product from the electronicshopping list that is out of stock), customers in the same geographicalregion as the retail store, customers with similar demographic data asthe customer, or the like.

In some embodiments, data from sensors within the retail environment mayalso be used to identify the product. This may include the one or moreimage sensors in the retail store, or additional sensors describedherein. For example, step 4240 may include analyzing the image data todetermine a location of the shopper during the product selection event.This may include the shopper's position within the retail store, adirection the shopper is facing, or other information that may helpidentify which product was selected. Accordingly, step 4240 may furtherinclude basing the identification of the product on the determinedlocation of the shopper.

In step 4250, process 4200 includes updating a virtual shopping cartassociated with the shopper in response to identification of theproduct. For example, this may include updating virtual shopping cart4050, as described above. The virtual shopping cart may be any form ofdata indicating which products have been selected by the shopper. Forexample, the virtual shopping cart may be a list, a table, an array, adatabase, or any other data structure suitable for tracking selecteditems. In some embodiments, the virtual shopping cart may be integratedinto the electronic shopping list. For example, the electronic shoppinglist may include fields or other data elements indicating whether itemson the electronic shopping list have been selected, which may alsoinclude a quantity of items selected. Consistent with the embodimentsdescribed above, the virtual shopping cart may further be associatedwith the customer. For example, if the shopper is a proxy shopper forthe customer, the virtual shopping cart may also be associated with thecustomer.

In some embodiments, process 4200 may include additional steps not shownin FIG. 42 . For example, process 4200 may further include automaticallyupdating the electronic shopping list to indicate that the product fromthe electronic shopping list has been selected. Accordingly, process4200 may further include using the updated electronic shopping list toidentify one or more additional products associated with one or moreadditional product selection events.

In some embodiments, the retail store may include a frictionlesscheckout process, which may require the shopper to be eligible to use.Process 4200 may further include maintaining a frictionless shoppingeligibility status associated with the shopper in response toidentification of the product. In some embodiments, the frictionlessshopping status may depend on a degree of confidence (e.g., a confidencescore, etc.) associated with the identification of the product.Accordingly, process 4200 may include determining an indicator of aconfidence level associated with the identification of the product andmaintaining the friction shopping eligibility status for the shopper ifthe indicator of the confidence level is above a predeterminedthreshold. In some embodiments, the predetermined threshold may dependon various factors associated with the product selection event. Forexample, the predetermined threshold may vary based on a product typeassociated with the product. Thus, a predetermined threshold for morevaluable or expensive items may be different than the predeterminedthreshold of other items. Similarly, the predetermined threshold mayvary based on location in a retail store. The predetermined thresholdmay vary based on other factors, such as a time of the day, a number ofcustomers in the store (i.e., how busy the store is), a number ofassociates present in the store, the identity of the shopper orcustomer, and more.

As described above, the disclosed embodiments may include the use ofelectronic shopping lists, which may indicate a list of items a customerintends to purchase. Accordingly, these electronic shopping lists may beused by a shopper when selecting products in a retail environment. Forexample, proxy shoppers who shop on behalf of one or more customers arebecoming increasingly common in retail environments. These proxyshoppers may select and purchase items based on an electronic shoppinglist associated with a customer. As another example, a customer maygenerate his or her own electronic shopping list and may use it toselect items in the retail store.

Typically, these electronic shopping lists are generated some time priorto products being selected in the retail environment. For example, acustomer may generate an electronic shopping list including items to beselected by a shopper hours, days, or even weeks ahead of the itemsbeing selected. In some cases, products that were indicated as beingavailable when the electronic shopping list was generated may no longerbe available at the time the order is being fulfilled. This leaves theshopper with the burden of determining whether a replacement item shouldbe selected and, if so, which available product best suit's thecustomer's needs or preferences. This situation may pose difficultiesfor a proxy shopper who might have little or no information regardingthe customer's needs or preferences.

The embodiments disclosed herein address these and other issues byautomatically selecting a best available substitute for a product thatis out of stock. Based on image data from image sensors included in theretail store, an inventory shortage for products on an electronicshopping list can be detected or even predicted ahead of time. Based onthe predicted inventory shortage, an electronic shopping list canautomatically be updated to reflect the shortage, which may ease theburden on the shopper at the time of fulfilment of the order.Accordingly, the disclosed embodiments provide, among other advantages,improved efficiency, convenience, and functionality over prior artelectronic shopping list management systems.

Consistent with the disclosed embodiments, a system, such as imageprocessing unit 130 may access an electronic shopping list of one ormore customers of a retail store. For example, as described above,server 135 may access electronic shopping list 3900, as shown in FIG. 39. Electronic shopping list 3900 may include at least one productassociated with a shopping order. As used herein, a shopping order mayrefer to a request, command, or instruction for one or more items to bepurchased. For example, electronic shopping list 3900 may includeproducts 3902, 3904, and 3906, which a customer may have selected forpurchase from a retail store. As described above, electronic shoppinglist 3900 may include information describing each product in the list,which may include one or more of a brand name, a model, a product typeor subtype (e.g., a product flavor, diet vs. regular, whole vs. 2% milk,etc.), a product category, a unit or packaging type (e.g., can, bottle,jar, box, etc.), a product size, a product identifier (e.g. a SKUnumber), or any other information that may help to identify a particularproduct.

To determine when an electronic shopping list needs to be updated, thedisclosed embodiments may include accessing image data from one or moreimage sensors included in a retail store. The images may be analyzed topredict an inventory shortage of one or more products included on anelectronic shopping list. FIG. 43 illustrates an example image 4300 thatmay be analyzed to predict an inventory shortage, consistent with thepresent disclosure. Image 4300 may be captured by an image capturedevice, such as camera 4030 or image capture device 125 (includingdevices 125A, 125B, 125C, 125D, 125E, 125F, or 125G), as describedabove. Image 4300 may include at least portion of a shelf that maydisplay products of a retail store. For example, image 4300 may includeat least a portion of shelf 4002, as shown in FIG. 43 . Server 135 maydetermine or predict an inventory shortage based on image 4300 orsimilar images captured by an image capture device. As used herein, aninventory shortage may refer to any condition in which a product isunavailable due to the physical quantity of the product within a retailenvironment. For example, an inventory shortage may occur when an itemintended to be purchased is out of stock. This may refer to the itembeing out of stock on a retail shelf, the item being out of stock withinthe entirety of the retail store (e.g., including a stocking room,delivery receiving area, etc.), or within the greater supply chain ofthe retail store. In some embodiments, the inventory shortage may bebased on a difference between a demand for a product and the current orplanned inventory. For example, if a quantity of 4 units of a particularproduct is included on one or more shopping lists, but only 3 units ofthe product are in stock, this may indicate an inventory shortageexists. Additional details regarding inventory shortages are providedbelow.

As shown in FIG. 43 , image 4300 may include representations of variousproducts stocked on shelf 4002. For example, this may include aparticular organic ketchup product 4310, which may correspond to product3904 included in electronic shopping list 3900. Various other products4320, 4330, 4340 and 4350 may also be placed on shelf 4002 and includedin image 4300. Server 135 may analyze image 4300 to determine aninventory shortage for product 4310. For example, server 135 may analyzeimage 4300 to determine a number of units of product 4310 displayed onshelf 4002. In some embodiments, this may include identifying a portion4312 of shelf 4002 dedicated to product 4310. Portion 4312 may beidentified in various ways. For example, portion 4312 may be identifiedby analyzing one or more previous images to determine where product 4310is typically stocked on shelf 4002. As another example, this may includeaccessing a planogram associated with shelf 4002, which may indicate anintended or planned area where product 4310 should be placed. In theexample shown in FIG. 43 , only one unit of product 4310 may beavailable. Accordingly, because the inventory for product 4310 is lessthan a quantity of item 3904 on electronic shopping list 3900, server135 may determine a shortage of product 4310 exists. As another example,an inventory shortage may be identified any time quantity of availableunits for product 4310 reaches zero or drops before a predeterminedthreshold (e.g., 5 units, 2 units, etc.).

While simplified examples are provided above, additional or more factorsmay equally be considered for identifying an inventory shortage. In someembodiments, an inventory shortage with respect to a product included inan electronic shopping list may be determined based on a quantity of theproduct included in one or more additional electronic shopping lists.For example, server 135 may access electronic shopping lists formultiple shoppers associated with retail environment 135. If the numberof units of a product included in the electronic shopping lists exceedsa detected inventory for a product on shelf 4002, an inventory shortagemay be predicted. In some embodiments, an inventory shortage may bedetermined with respect to one or more particular electronic shoppinglists. For example, each electronic shopping list may be associated witha specified or predicted time at which a shopper associated with therespective shopping list will select a product. In some embodiments, thetimes may be input by a shopper or customer when placing an order orgenerating the shopping list. As another example, the times may bescheduled by the retail store (e.g., indicating when a shopping orderwill be available for pickup, etc.).

In some embodiments, the times may be predicted based on otherinformation. For example, an order may be estimated to be fulfilledwithin 24 hours of being placed, within 2 weeks of the time a previousorder for a customer was fulfilled, or various other predeterminedtimeframes. In some embodiments, the expected or predicted time tofulfilment of an order may depend on a type of shopper. For example, asdescribed above, a shopper may be the customer, or may be anotherentity, such as a proxy shopper, a store associate or another entitythat may shop in place of a customer. A store associate may be expectedto fulfil orders sooner than other types (or vice versa). Or, as anotherexample, a store associate may be expected to fulfil orders atparticular times based on a fulfilment schedule associated with theretail store. In some embodiments, rather than based on a category ofshopper, server 135 may store and access historical information for aparticular shopper or customer to predict a time for an order to befulfilled. For example, if a particular shopper usually shops onWednesday mornings, an order may be expected to be fulfilled during themorning of the next upcoming Wednesday. Based on the order in whichshopping orders are expected to be fulfilled, an inventory shortage maybe predicted with respect to only later electronic shopping lists wherean inventory for a product is predicted to be depleted based on thequantity of a product included in prior electronic shopping lists.

Various other factors may also be used to determine or predict aninventory shortage. In some embodiments, information about a product maybe tracked over time to predict an inventory shortage. As one example,an inventory of an item may be tracked over time to determine a rate atwhich a product is removed from a retail shelf. For example, this mayinclude detecting a quantity of product 4310 on shelf 4002 in multipleimages over time. Server 135 may determine an expected inventory for theproduct at an estimated fulfilment time for an electronic shopping listassuming the product continues to be selected at the same rate. Thisexpected inventory may be compared to the quantity of a product includedin an electronic shopping list to identify an inventory shortage.Similarly, the inventory may be tracked over time based on a detectedrate at which the product is added to a virtual cart of one or moreshoppers, or is purchased by one or more shoppers.

In some embodiments, a product delivery schedule for a product may alsobe taken into consideration. For example, server 135 may access adelivery schedule which may indicate a predicted time at whichadditional products will be received at the retail store. This may alsoinclude a scheduled quantity of a particular product to be delivered.This information may be used in conjunction with any of the variousexamples described above for determining an inventory shortage. Forexample, if 3 units of a product are included on a first shopping listand an additional unit of a product is included on a second shoppinglist, and only 3 units are detected as being in stock, this maytypically trigger an inventory shortage prediction for the secondelectronic shopping list. However, if a shipment for the product isexpected to be delivered prior to an expected fulfillment time for thesecond shopping list, no inventory shortage may be predicted.

Based on the predicted inventory shortage, server 135 may automaticallyupdate one or more electronic shopping lists. Accordingly, if aninventory shortage is predicted to occur before an order associated withthe electronic shopping list is expected to be fulfilled, the updatedshopping list may be used in place of the original shopping list. Theupdated shopping list may be modified to ease the burden on a shopperdeciding how to respond to the inventory shortage. Accordingly, theupdated shopping list may be presented to the shopper prior to or duringfulfilment of the order.

The electronic shopping list may be updated in various ways based on thepredicted inventory shortage. FIG. 44 is a diagrammatic illustration ofvarious updates to electronic shopping list 3900 that may be performed,consistent with the present disclosure. As shown in FIG. 44 , electronicshopping list 3900 may include item 3904, which may correspond toproduct 4310 shown in image 4300. As described above, an inventoryshortage associated with product 4310 may be predicted. Server 135 maytake various actions based on the predicted inventory shortage,including updates 4420, 4430, 4440, and 4450, as shown. In someembodiments, the updates may include removing an item from theelectronic shopping list. For example, this may include removing item3904 from electronic shopping list 3900 as illustrated in update 4420.This may be due to no units of product 3410 being available, aninsufficient number of products 3410 being available to fulfil thequantity indicated in electronic shopping list 3900, or various otherconditions described herein. As another example, the update may includeupdating a quantity of a product included on the electronic shoppinglist. For example, as illustrated in update 4430, this may includereducing the number of item 3904 included in electronic shopping list3900 from 2 units to 1 unit. This reduction may be due to a predictionthat only 1 unit of product 3410 will be available at the time an orderassociated with electronic shopping list 3900 is fulfilled, asillustrated in FIG. 43 .

In some embodiments, the update to electronic shopping list 3900 mayinclude substituting one product for another product in the list. Forexample, as illustrated by update 4440, server 135 may replace item 3904with a replacement item 4442. In some embodiments, this may includevisually identifying item 4442 as a substitute item, for example, byincluding a visual indicator 4444. Various other methods for identifyingitem 4442 as a substitute may be used, such as displaying item 4442 in adifferent font, different color, in a separate section of electronicshopping list 3900, or the like.

The product to be substituted into electronic shopping list 3900 may beidentified in various ways. Substitute item 4442 may be selected fromthe same product category as the original item. In this example,replacement item 4442 may correspond to product 4330 shown in FIG. 43 .In some embodiments, substitute item 4442 may be selected to have atleast one attribute in common with item 3904. For example, product 4330may be a regular bottle of ketchup of the same brand and size as product4310. In some embodiments, substitute item 4442 may be selected tomaximize the number of attributes in common with item 3904. For example,product 4330 may be selected over product 4340, which may be of adifferent product type (e.g., non-organic) and may be of a differentbrand than product 4310. In some embodiments, one or more attributes maybe ranked or weighted higher than others. For example, it may be moreimportant that substitute item 4442 have the same size and same producttype (e.g., organic) than the same brand, or vice versa. In someembodiments, the rankings or relative weights of attributes may becustomer-specific. For example, based on a shopping history for acustomer, customer preference inputs, or the like, server 135 mayidentify certain attributes as being more important to a particularcustomer than others. Alternatively or additionally, the ranking orrelative weights of attributes may vary based on a product category, ageographic location of a retail store, demographic information for ashopper (e.g., age, gender, etc.), or any other relevant factors.

In some embodiments, substitute item 4442 may be identified based on ashopping history for a shopper or customer. For example, if a customerpurchases product 4330 more frequently than products 4320, 4340, or4350, product 4330 may be selected for substitute item 4442. Theshopping history may represent an entire shopping history for a customeror may be a subset of purchases. For example, the shopping history maybe limited to occasions where product 4310 was out of stock to determinewhich product the customer purchases as a substitute. As other examples,the shopping history may be limited to a number of most recenttransactions, transactions occurring at the same or similar time of dayas the current shop, transactions occurring on the same day of the weekor time of month as the current shop, transactions that also includeanother item of a particular product category (e.g., hot dogs, hamburgerbuns, mustard, etc.), or various other factors. In some embodiments, theshopping history may not be tied to a particular customer or shopper,but may be for all customers of a retail store, customers of a similardemographic as the current customer, recent transactions at the retailstore, or the like.

According to some embodiments, substitute item 4442 may be selectedbased on product affinity information associated with the customer. Forexample, server 135 may access product affinity data 4140, as describedabove. For example, the product affinity information may be a ranking ofhistorical purchases by a customer, which may indicate an affinity for aparticular product. The product affinity information may also indicatean affinity for a particular product type. For example, if the customerconsistently buys organic products, bulk products, low fat or fat-freevarieties, or the like, it may indicate an affinity between the customerand this product type. As another example, the product affinityinformation may be based on a preference or other input from thecustomer. In some embodiments, the product affinity information mayindicate a pairwise affinity between products or product types. Forexample, customers who commonly buy Brand X of deodorant may be morelikely to buy Brand X (or even Brand Y) of shaving cream. As anotherexample, customers who frequently buy organic products may commonlypurchase sulfate-free products.

In some embodiments, the inventory of one or more other products may beconsidered in selecting substitute item 4442. For example, server 135may identify product 4320 as being the best substitute for product 4310,but an inventory shortage with respect to product 4320 may also beidentified with respect to product 4320. The inventory shortage forproduct 4320 may be predicted using the various methods described abovewith respect to product 4310. For example, although 2 units of product4320 are shown as being stocked in image 4300, product 4320 may beincluded on one or more electronic shopping lists expected to befulfilled before electronic shopping list 3900. Accordingly, product4330 may be selected as a substitute instead. In some embodiments, aranking of substitute products may be determined, which may be based onany of the methods for selecting a substitute product described herein.Accordingly, this ranking may be used to select substitute item 4442 inthe event that one or more products are also associated with predictedinventory shortages.

In some embodiments, substitute item 4442 may be selected based on anoptimization process for multiple electronic shopping lists. Forexample, the customer associated with electronic shopping list 3900 maynot have a strong preference or requirement that products be organic,whereas a second customer associated with an electronic shopping listexpected to be fulfilled after electronic shopping list 3900 may have arequirement for organic products. If product 4320 is selected assubstitute item 4442 in electronic shopping list 3900, this may leave noorganic products available for the second customer. Accordingly, server135 may select product 4330 as substitute item 4442 to leave product4320 available for the second customer. As another example, this mayinclude suggesting or performing a substitution on an electronicshopping list not associated with a predicted inventory shortage inorder to resolve or better address predicted inventory shortages onother electronic shopping lists. For example, if product 4310 ispredicted to be available at the time a first electronic shopping listis expected to be fulfilled but not at the time a second electronicshopping list is expected to be fulfilled, server 135 may suggest asubstitution of product 4330 on the first electronic shopping list if acustomer associated with the second electronic shopping list has agreater preference for organic products than a customer associated withthe first electronic shopping list. As one skilled in the art wouldrecognize, more complex optimizations may be performed for as a greaternumber of electronic shopping lists are considered.

According to some embodiments, substitute item 4442 may be selected atleast in part based on an input from a customer or shopper associatedwith electronic shopping list 3900. For example, server 135 may send anotice to the customer regarding the predicted inventory shortage andmay receive a selection from the customer of one or more alternateproducts to include as substitute item 4442. In some embodiments, thismay include a request for the customer to select the substitute productfrom a list of all available products in the retail store, or a subsetof products (e.g., products of the same product category or type, etc.).Alternatively or additionally, server 135 may suggest one or moreproducts and the customer may confirm a suggested substitute or selectfrom multiple substitute products. For example, server 135 may presentan interface 4452 to receive a selection from a customer or shopper frommultiple suggested substitute products. Interface 4452 may be integratedinto a display of electronic shopping list 3900, as shown in FIG. 44 ,or may be displayed as part of a separate interface or menu.

While various updates 4420, 4430, 4440, and 4450 are provided by way ofexample, it is to be understood that various other updates to electronicshopping list 3900 may be made, consistent with the present disclosure.In some embodiments, multiple updates may be made to the same electronicshopping list. For example, if only one of product 4310 is available,the updates may include reducing the quantity of item 3904 by one andadding a quantity of one substitute item 4442 to electronic shoppinglist 3900. In some embodiments, server 135 may make updates to more thanone electronic shopping list. For example, this may include substitutingan item on a first list and removing an item from a second list.

As an alternative to or in addition to the updates described above,various other actions may be taken based on identification of aninventory shortage. For example, this may include automatically sendinga notice to a customer regarding an automatic update to the electronicshopping list. This may include transmitting a notice to device 3910 ordevice 125. Similarly, server 135 may transmit a notice or alertidentifying the predicted shortage to a store associate, a storemanager, a supplier associate with the product, an inventory managementsystem, or the like. In some embodiments, server 135 may automaticallysubmit a restocking request, submitting an order for additionalproducts, marking a product as unavailable on a website or otherdatabase, or other actions to mitigate the predicted inventory shortage.

In some embodiments, server 135 may generate a shopping path associatedwith the updated electronic shopping list. The shopping path mayrepresent a suggested or optimized route a shopper should take through aretail store to select the items in the electronic shopping list. FIG.45 illustrates an example shopping path 4510 that may be generated basedon an updated electronic shopping list, consistent with the presentdisclosure. For example, an inventory shortage may be predicted withrespect to product 4522 and, as a result, product 4512 may besubstituted in the electronic shopping list for product 4522 asdescribed above. Accordingly, shopping path 4510 may be generated toinclude substitute product 4512, as shown in FIG. 45 . As anotherexample, product 4522 may be removed and the shopping path may begenerated not to include either of products 4512 and 4522. In someembodiments, generating the shopping path may include updating aprevious shopping path. For example, if product 4512 is substituted forproduct 4522, a previous shopping path 4520 may be updated to shoppingpath 4510, as shown. While shopping path 4510 is shown as a pathoverlaid on a map 4500 of a retail store, the shopping path may berepresented in any suitable way to show flow or order of selectionsthrough a retail store. For example, the shopping path may include aseries of locations (e.g., coordinates, store aisles or sections, etc.),an ordered list of items, a display in an augmented reality device, orthe like. Generating the shopping path may include making the shoppingpath available to a customer, a store associated, a shopper, or anotherentity associated with the electronic shopping list.

Consistent with the disclosed embodiments, image data may becontinuously or periodically analyzed to predict inventory shortageevents. For example, this may include continuous monitoring of imageframes, analyzing images at specified intervals (e.g., every minute, 10minutes, every hour, every 6 hours, etc.), after a trigger event (e.g.,when a new item is added to an electronic shopping list, when a newelectronic shopping list is submitted or created, or various otherevents). In some embodiments, various events may be detected thatresolve or negate a predicted inventory shortage. For example, server135 may identify a restocking event associated with a product, which mayresolve a predicted inventory shortage. As other examples, a customermay return a product to a shelf or a retail store, a delivery orrestocking of a product may be scheduled, an intervening electronicshopping list may be canceled, a product may be removed from anintervening electronic shopping list, or other events may occur thatresolve an inventory shortage. Accordingly, server 135 may update theupdated shopping list based on the restocking event or other event. Forexample, this may include adding a product back to the electronicshopping list, removing a substitute item from the electronic shoppinglist, increasing a quantity of an item, or the like.

FIG. 46 is a flowchart of an exemplary method for automatically updatingelectronic shopping lists of customers of retail stores, consistent withthe present disclosure. Process 4600 may be performed by at least oneprocessing device of a server, such as processing device 302, asdescribed above. In some embodiments, some or all of process 4600 may beperformed by a different device associated with system 100. In someembodiments, a non-transitory computer readable medium may containinstructions that when executed by a processor cause the processor toperform process 4600. Further, process 4600 is not necessarily limitedto the steps shown in FIG. 46 , and any steps or processes of thevarious embodiments described throughout the present disclosure may alsobe included in process 4600, including those described above withrespect to FIGS. 43, 44, and 45 .

In step 4610, process 4600 includes accessing an electronic shoppinglist of a customer of a retail store. For example, this may includeaccessing electronic shopping list 3900 described above. Consistent withthe disclosed embodiments, the electronic shopping list may include atleast one product associated with a shopping order. The electronicshopping list may be stored in any location or plurality of locationsaccessible to server 135. For example, accessing the electronic shoppinglist may include accessing the electronic shopping list from a server,from a cloud storage platform, from an online website or other web-basedplatform, a local device memory, or any other storage location. Theelectronic shopping list may be generated in various ways. In someembodiments, the electronic shopping list may be generated by thecustomer. For example, the customer may select one or more products forinclusion in the electronic shopping list through a user interface.Alternatively or additionally, the electronic shopping list may beautomatically generated. For example, the electronic shopping list maybe generated automatically based on the customer's shopping history inthe retail store.

Fulfilment of the shopping order may be completed by various entities,including the customer, a store associated of the retail store, a proxyshopper, or the like. In some embodiments, the electronic shopping listmay be associated with an expected fulfillment time. For example, thefulfilment time may depend on a type of the shopper expected to fulfillthe shopping order. The fulfilment time may be estimated or determinedbased on other factors, as described in greater detail above.

In step 4620, process 4600 includes receiving image data from aplurality of image sensors mounted in the retail store. For example,this may include receiving image 4300, which may be captured by camera4030. The image sensors may be positioned such that an inventory foritems may be ascertained using the image data. The image data may beprocessed using an image processing unit 130 as described above.

In step 4630, process 4600 includes analyzing the image data to predictan inventory shortage of the at least one product included on theelectronic shopping list. The predicted inventory shortage may haveoccurred or may be expected to occur prior to fulfillment of theshopping order. The inventory shortage may be predicted in various ways,as described above. For example, the inventory shortage may be predictedbased on a determination that the at least one product is currently outof stock. Various other factors may also be considered to predict theinventory shortage, including a detected rate at which the at least oneproduct is added to a virtual cart, a detected rate at which the atleast one product is removed from a retail shelf, a quantity of the atleast one product included in electronic shopping lists associated withadditional shopping orders expected to be fulfilled prior to fulfilmentof the shopping order, a product delivery schedule associated with theretail store, or any other information that may indicate that the atleast one product will not be available at the time of fulfilment of theshopping order. In some examples, a convolution of at least part of theimage data may be calculated. Further, in response to a first value ofthe calculated convolution of the at least part of the image data, aninventory shortage of the at least one product included on theelectronic shopping list may be predicted, and in response to a secondvalue of the calculated convolution of the at least part of the imagedata, no inventory shortage of the at least one product included on theelectronic shopping list may be predicted. For example, the inventoryshortage of the at least one product included on the electronic shoppinglist may be predicted when the value of the calculated convolution ofthe at least part of the image data is a particular group of values.

In step 4640, process 4600 includes automatically updating theelectronic shopping list based on the predicted inventory shortage ofthe at least one product. The electronic shopping list may be updated ina variety of ways, as described in detail above. For example,automatically updating the electronic shopping list may include removingthe at least one product from the electronic shopping list, as describedabove with respect to update 4420. Similarly, automatically updating theelectronic shopping list may include reducing a quantity of the at leastone product included on the electronic shopping list, as described abovewith respect to update 4430. In some embodiments, updating theelectronic shopping list may include substituting the at least oneproduct with a second product. For example, the second product may beselected based on a shopping history associated with the customer orproduct affinity information associated with the customer, as describedabove. In some embodiments, the second product may be selected based onan inventory of a third product, which may differ from the at least oneproduct and from the second product. For example, an ordinary substitutefor the at least one product may be the third product, but a lowinventory of the third product may cause a substitute by the secondproduct. The inventory of the third product may include an inventoryprior to fulfillment of the shopping order, a predicted inventory at anexpected time of the fulfillment of the shopping order, a currentinventory, or various other inventories. In some embodiments, theinventory of the third product may be determined based on imageanalysis, as performed in step 4630.

In some embodiments, step 4640 may include receiving input from thecustomer regarding the substitute product. For example, step 4640 mayinclude sending a notice to the customer regarding the predictedinventory shortage and receiving a selection from the customer of the atleast one alternate product. Based on the selection, step 4640 mayinclude substituting the at least one product on the electronic shoppinglist with the at least one alternate product. In some embodiments, step4640 may further include identifying to the customer the at least onealternate product. Accordingly, the user may confirm or select from thepresented at least one alternate product. In some embodiments, the atleast one alternate product may be identified based on a shoppinghistory associated with the customer. Alternatively or additionally, theat least one alternate product may be identified based on productaffinity information associated with the customer.

In some embodiments, process 4600 may include additional steps not shownin FIG. 46 . For example, process 4600 may include automatically sendinga notice to the customer regarding the automatic update to theelectronic shopping list. Similarly, process 4600 may includeautomatically sending a notice to a store associate, a store manager, asupplier of the at least one product, or other entity indicating thepredicted inventory shortage. In some embodiments, process 4600 mayfurther include taking one or more actions to resolve the predictedinventory shortage. For example, this may include automaticallysubmitting an order for the at least one product, automaticallyscheduling a restocking event associated with the at least one product,removing a product from being displayed as available on an onlinewebsite or other list of products, or various other actions.

In some embodiments, process 4600 may include generating a shopping pathassociated with the updated electronic shopping list. For example, thismay include generating shopping path 4510, as described above withrespect to FIG. 45 . In some embodiments, generating the shopping pathmay include updating a previously generated shopping path based on theupdated electronic shopping list. For example, this may include removinga portion of a path associated with the at least one product,recalculating an optimized path through a retail environment, or similarupdates. The generated shopping path may be provided to the customer,the shopper (e.g., a proxy shopper), a store associate, or otherentities associated with the electronic shopping list. For example, thegenerated shopping path may be displayed on a user interface, overlaidon a map of the retail store, presented as a series of locations withinthe retail store, displayed through an augmented reality device,presented audibly, or presented through various other means.

As described above, process 4600 may further include updating theupdated shopping list as additional information is received. Forexample, process 4600 may include receiving additional image data fromthe plurality of image sensors, analyzing the additional image data toidentify a restocking event associated with the at least one product,and updating the updated shopping list based on the restocking event.For example, updating the updated shopping list may include adding theat least one product back to the electronic shopping list, increasing aquantity of the at least one product on the electronic shopping list,removing a substitute item added to the electronic shopping list, or thelike. In some embodiments, the updated shopping list may be updatedbased on other information, such as the at least one product beingdetected as being returned to the shelf (e.g., by a customer, etc.), theat least one product being returned to the retail store, a delivery ofthe at least one product being scheduled, or any other information thatmay affect the predicted inventory shortage.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, 4K Ultra HD Blu-ray,or other optical drive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

1-84. (canceled)
 85. A non-transitory computer-readable medium includinginstructions that, when executed by a processor, cause the processor toperform a method for identifying products removed from bulk packaging,the method comprising: receiving one or more images acquired by a cameraarranged to capture interactions between a shopper and one or more bulkpackages each configured to contain a plurality of products; analyzingthe one or more images to identify the shopper and a particular bulkpackage among the one or more bulk packages with which the identifiedshopper interacted; receiving an output from at least one sensorconfigured to monitor changes associated with the particular bulkpackage; analyzing the output to determine a quantity of productsremoved from the particular bulk package by the identified shopper; andupdating a virtual shopping cart associated with the identified shopperto include the determined quantity of products and an indication of aproduct type associated with the particular bulk package.
 86. Thenon-transitory computer-readable medium of claim 85, wherein the atleast one sensor includes a weight sensor configured to monitor changesin a weight of the particular bulk package.
 87. The non-transitorycomputer-readable medium of claim 85, wherein the one or more bulkpackages include shelf-ready packages configured to hold a plurality ofproducts each weighing less than 1 kg.
 88. The non-transitorycomputer-readable medium of claim 85, wherein the one or more imagesalone are insufficient for determining whether products were removedfrom the particular bulk package.
 89. The non-transitorycomputer-readable medium of claim 85, wherein the output from the atleast one sensor alone is insufficient for determining whether productswere removed from the particular bulk package or from another bulkpackage among the one or more bulk packages.
 90. The non-transitorycomputer-readable medium of claim 85, wherein the output from the atleast one sensor alone is insufficient for determining an identificationof a shopper that removes a product from the particular bulk package.91. The non-transitory computer-readable medium of claim 85, wherein theat least one sensor is disposed on a retail shelf.
 92. Thenon-transitory computer-readable medium of claim 85, wherein the atleast one sensor is disposed on a retail shelf under the particular bulkpackage.
 93. The non-transitory computer-readable medium of claim 85,wherein the method further comprises obtaining information indicative ofa first weight of a single product associated with a first bulk packageamong the one or more bulk packages and a second weight of a singleproduct associated with a second bulk package among the one or more bulkpackages, and wherein the determination of the quantity of the productsremoved from the particular bulk package by the identified shopper isbased, at least in part, on the first weight and the second weight. 94.The non-transitory computer-readable medium of claim 85, wherein themethod further comprises delivering a notification to the identifiedshopper that the identified shopper is not eligible for frictionlesscheckout if the quantity of products removed from the particular bulkpackage cannot be conclusively determined.
 95. The non-transitorycomputer-readable medium of claim 85, wherein the method furthercomprises analyzing the one or more images to determine the product typeassociated with the particular bulk package.
 96. A system foridentifying products removed from bulk packaging, the system comprising:at least one processing unit configured to: receive one or more imagesacquired by a camera arranged to capture interactions between a shopperand one or more bulk packages each configured to contain a plurality ofproducts; analyze the one or more images to identify the shopper and aparticular bulk package among the one or more bulk packages with whichthe identified shopper interacted; receive an output from at least onesensor configured to monitor changes associated with the particular bulkpackage; analyze the output to determine a quantity of products removedfrom the particular bulk package by the identified shopper; and update avirtual shopping cart associated with the identified shopper to includethe determined quantity of products and an indication of a product typeassociated with the particular bulk package.
 97. A non-transitorycomputer-readable medium including instructions that when executed by aprocessor cause the processor to perform a method for identifyingproducts removed from bulk packaging, the method comprising: receivingan output from one or more spatial sensors arranged to captureinteractions between a shopper and one or more bulk packages eachconfigured to contain a plurality of products; analyzing the output fromthe one or more sensors to identify the shopper and a particular bulkpackage among the one or more bulk packages with which the identifiedshopper interacted; receiving an output from at least one additionalsensor configured to monitor changes associated with the particular bulkpackage; analyzing the output from the at least one additional sensor todetermine a quantity of products removed from the particular bulkpackage by the identified shopper; and updating a virtual shopping cartassociated with the identified shopper to include the determinedquantity of products and an indication of a product type associated withthe particular bulk package.
 98. The non-transitory computer-readablemedium of claim 97, wherein the one or more spatial sensors include aLIDAR system.
 99. The non-transitory computer-readable medium of claim97, wherein the one or more spatial sensors include a camera.
 100. Thenon-transitory computer-readable medium of claim 97, wherein the one ormore spatial sensors include an RFID reader.
 101. The non-transitorycomputer-readable medium of claim 97, wherein the one or more spatialsensors include a piezoresistive sensor.
 102. The non-transitorycomputer-readable medium of claim 97, wherein the at least oneadditional sensor includes a weight sensor configured to monitor changesin a weight of the particular bulk package.
 103. The non-transitorycomputer-readable medium of claim 97, wherein the at least oneadditional sensor includes a pressure sensor.
 104. A method foridentifying products removed from bulk packaging, the method comprising:receiving an output from one or more spatial sensors arranged to captureinteractions between a shopper and one or more bulk packages eachconfigured to contain a plurality of products; analyzing the output fromthe one or more sensors to identify the shopper and a particular bulkpackage among the one or more bulk packages with which the identifiedshopper interacted; receiving an output from at least one additionalsensor configured to monitor changes associated with the particular bulkpackage; analyzing the output from the at least one additional sensor todetermine a quantity of products removed from the particular bulkpackage by the identified shopper; and updating a virtual shopping cartassociated with the identified shopper to include the determinedquantity of products and an indication of a product type associated withthe particular bulk package. 105-224. (canceled)